This is the blog of David M. Raab, long-time marketing technology consultant and analyst. Mr. Raab is Principal at Raab Associates Inc. The blog is named for the Customer Experience Matrix, a tool to visualize marketing and operational interactions between a company and its customers.
It seems a bit soon to wax nostalgic about the good old days of the Customer Data Platform industry. But the industry has become so bewilderingly diverse in recent years that it is tempting to recall simpler times when all a new product needed to promise was making it easier to unify their customer data. That was way back in 2016.
Stride is clearly a next-generation CDP, on the market for under two years and including the analytic and orchestration tools common to new CDP systems. But its primary focus on data aggregation and access makes it feel something like a throwback. How did that happen?
The tale begins with RelateIQ, an AI startup purchased by Salesforce in 2014. Stride’s co-founders came along with the deal and became Salesforce employees. They soon discovered that existing Salesforce tools couldn’t collect and present all the customer data they wanted – Web site browsing history in particular. They left Salesforce in early 2016 to build a product to fill this gap. Stride, the result of their efforts, reached the market in mid-2017.
The design goal for Stride was a system that could ingest and make available nearly any type of data with little technical effort and then activate that data by sharing customer lists with delivery systems. That's what they delivered.
It all starts with data. Stride can ingest nearly any type of data with minimal preparation, loading batch files, SQL transactions, streaming data, Web tags feeds, and other sources into SQL tables or S3 buckets. Set-up requires little more than connections to source systems: the ingested data is flattened and then stored in pretty much its original form in what the company describes as a “semi-structured, flexible schema” that can accommodate any source and set of objects. Initial deployment can be completed in a couple of weeks after the source data is made available.
Users can enrich the inputs by adding custom objects, attributes, and events. These are built in structured interfaces designed for non-technical users. Stride doesn’t have its own identity matching processes, although users can map relationships between personal identifiers in different systems and the system will store links between identifiers when it finds them. Stride can perform simple deterministic matching, such as connecting emails to Web cookies on the devices used to open them.
Results are placed in the Snowflake relational database, an increasingly popular tool for cloud-based data warehouse applications. Users can decide which items be exposed for analysis and segmentation. Stride provides extensive tools to explore the loaded data, including a detailed view of all data for an individual customer.
External systems can query the Stride data through APIs. In addition, Stride provides two sets of integrated capabilties: Audiences and Programs.
Audiences lets users build customer segments using the same drop-down interface that creates custom attributes and events. An audience can be defined as a single segment or as a waterfall sequence of segments, where each is customer assigned to the first segment they match. Segment membership is updated within minutes as new data enters the system.
Audience reports can show movement of customers between segments, can compare different segments against each other, and show segment member statistics such as average order value and number of messages received. Users can analyze or extract subsets within a segment, such as new entrants. Segments can be shared with external systems, including Facebook, Google, and Twitter advertising products. Shared segments are updated automatically as members are added or removed.
Programs assign actions to execute in external systems. Each program has eligibility criteria, behaviors that define entry conditions, actions to take when a behavior occurs, and behaviors that remove customers from the program. Eligibility criteria can look across all events to do things such as limit contact frequency within a specified time period. The system can’t yet prioritize programs to ensure the most important messages are sent first, although Stride is working on it.
Each program can include a sequence of actions which occur over time. Actions can also have their own qualification conditions and be chosen in a waterfall sequence. Actions send instructions and data to external delivery systems. Instructions can trigger a single message or assign a customer to a campaign or journey. The system has standard connectors for major email platforms and marketing clouds as well as advertising systems. Behaviors can include events that trigger actions in near-real-time.
Program reports describe program-generated activities and dropout rates with reasons for each step in a multi-step program. Users can also create split tests within a program as well as global control groups to provide a baseline for measuring program impact.
One feature that marks Strike as a next-generation CDP is pricing. Most early CDPs were targeted at enterprise buyers and rarely sold for less than $250,000 per year. Stride pricing is based on number of customer profiles and can be as low as $50,000 per year for a company with under 100,000 contacts. The largest client is over 20 million. Clients are spread across multiple industries including retail, travel, media, financial services, and healthcare.
I returned earlier this week from a sequence of workshops, speeches, and meetings in Europe, all focused on Customer Data Platforms. Here are some observations:
- The European CDP market is indeed behind the U.S. My own conversations are with people who already care about CDPs, so they're a very skewed sample. But vendors, consultants, agencies, and marketers I spoke with mostly agreed that the larger community is just beginning to hear about the concept. Many are seeking to position themselves as early adopters or experts, sensing a big business opportunity.
- Separate martech staff is rare. Nearly every large and mid-size company that I see in the U.S today has someone in charge of marketing technology, and often an entire team of marketing technologists reporting to the CMO. I was told this is much less common in Europe and personally didn’t meet anyone with a martech title. Nor did I hear about powerful IT departments taking charge. Rather, it seems that marketers still mostly act on their own, which is how it worked in the U.S. until a few years ago. I did have the impression that European marketers rely more heavily on specialist consultants to help them out, but that might be biased by the fact that many of my meetings were with consultants.
- DMP means something different in Europe. We consistently heard that marketers throughout Europe, and especially in France, were oversold several years ago on Data Management Platforms as a complete solution to handle all customer data needs. This contrasts quite sharply with the U.S., where DMPs have in most cases been understood as limited to serving up digital ad audiences. European DMPs are now recognized as having failed to deliver on the broader promise, which is beyond their technical capabilities. The resulting backlash greatly damaged the image of DMP products and has left marketers looking for a new solution that is truly capable of meeting their needs. Many recognize that CDP could be this solution and are intrigued. But they're also skeptical and worried that they’ll be fooled again. This makes it harder for CDP vendors to sell their products. On the bright side, it also means the problem CDPs address is already well understood.
- CRM also means something different. Back when Bill Clinton was president, CRM was described as a trinity of sales, service, and marketing systems, with marketing much weaker than the other two. It commonly referred to B2C as well as B2B. Later, in the U.S., the term came to be more associated with B2B sales and customer service in general and the Salesforce.com Sales cloud in particular. In Europe, CRM is used very broadly to mean any and all customer data, extending far beyond sales, service, and marketing, and including both B2B and B2C. On reflection, I may have recently been hearing people in the U.S. apply the term more broadly as well.
- Use cases are everything. We’ve seen a huge demand to present CDP use cases in the U.S. But it seemed even more pressing in Europe, perhaps because understanding of the CDP concept is weaker. One difference seemed to be that Europeans are willing to interact with vendors as a way of learning: while many U.S. buyers actively avoid vendors during the early stages of the purchase process, we heard quite a few requests in Europe to see detailed demonstrations of how individual vendors accomplish specific tasks. Maybe the European salespeople do a better job of being consultative, or maybe European buyers are less determined to find things out on their own. Or maybe it’s just my imagination.
- Immediate ROI is required. We also found a greater focus in Europe on use cases that tie directly to marketing programs, as opposed to the analytical use cases that are most common starting CDP applications in the U.S. The reason seems to be that European buyers are more insistent on finding a specific financial justification for their investment. Many U.S. buyers will accept a broader strategic justification and start with analytical use cases. This may be why European CDP vendors are more likely to offer a full scope of data, analytical, and campaign capabilities, since buying them in a single package makes it easier to tie new marketing programs directly to the CDP investment.
- National markets are distinct. Some of the big U.S. vendors are present throughout Europe, but many local vendors are largely limited to individual markets. We had some sense of this beforehand but the isolation was greater than expected. The French market in particular has its own ecosystem of CDPs and other types of software that have a major domestic position but little presence elsewhere. The Netherlands, German, Nordic and UK markets show more cross-over, probably because English is widely spoken in all of them. The greater interest in CDP-based marketing programs may also encourage this, since marketing programs are closely tied to specific local markets.
- GDPR hasn’t caused much change. We had some discussions about using CDP for GDPR compliance but privacy constraints in general rarely come up. The common attitude was that privacy rules were already tight in the countries we visited (Belgium, Netherlands, Germany and France), so GDPR hadn’t required significant adjustments. There was also some discussion about waiting to see how the rules are actually enforced, which might require further adjustments if the regulators are strict.
While these differences are interesting, they’re also fairly minor. Over all, the European marketers were feeling the same pressures as their U.S. counterparts to create unified data for better customer experiences. So while each market will have its own quirks and proceed at a its own pace, it looks like they’ll follow the same general path as the U.S.
I review a lot of surveys -- easily a dozen each week. Mostly they go into a big file which I mine occasionally for helpful factoids to spice up a paper or presentation. Sometimes I take a more thorough tour to look at some bigger issues. Today is one of those days.
Specifically, I was prepping for a presentation in Amsterdam, which meant I needed to present general industry trends and then see what is different in Europe. This turned out to be pretty interesting. But I assembled vastly more data than I could include in any presentation where shackles were not involved. So I'm sharing it all here with you instead.
(I've also packaged it all in a paper for the Customer Data Platform Institute, available here. Much more convenient than copying this blog post if you want a reference copy.)
Note that there's more information on sources at the end of this post. For now, let's just get to the good stuff.
Consumer Attitudes: Personalization
If marketers hold any truth to be self-evident, it’s that today’s consumers want and expect personalization. The reality is a bit different and depends greatly on the definition of “personalization”. The majority of consumers believe they receive personalized service, but many fewer expect personalized experiences. What they do expect is consistent service, shared information, and being identified as repeat customers. In other words, they expect you to know who they are and to use that data to serve them – for example, by being aware of past purchases and problems. But they don’t necessarily expect you to make personalized offers or otherwise personalize their experience.
We do see quite consistently that European consumers have lower expectations for all kinds of personalization.
Whether or not consumers expect personalization, it can still be a competitive advantage to provide it. The majority of consumers do say they’re more loyal to brands that understand them and provide good service, and more likely to stop doing business with brands with poor service. But, again, the focus seems to be more on service than proactive personalization: barely one quarter of consumers said that anticipating needs is the most important part of personalization. This may come as a shock to marketers who have put anticipating needs at the top of their list of reasons to do personalization.
These results shouldn’t be read as a reason to ignore customer needs. Companies get more revenue when they offer customers what they want, whether or not the customer expects it.
We again see that European consumers place slightly less weight than U.S. consumers on personalization, although the difference is less pronounced than with expectations. One interpretation would be that European consumers don’t expect personalized treatments and thus don’t factor it into their behavior.
Consumer Attitudes: Privacy
Marketers know they need to balance personalization against privacy. We’ve just seen that consumer interest in personalization may not be quite as high we thought. By contrast, consumers show great interest in privacy, both in general and specifically in relation to marketing. More than three-quarters don’t want companies to market to them based on personal data. Fewer than half would trade their data for personalized service, even though that’s the reason most companies give for collecting it. Although European consumers show slightly less concern about privacy in general, they are more opposed than U.S. consumers to letting companies use their data for marketing. This is consistent with the personalization results: if European consumers place less value on personalization, it makes sense that they’d be less willing to share their personal data to enable personalized treatments.
Looking beyond personalization to the broader question of trust, we again see that Europeans place less trust in business than U.S. consumers. An astonishing 68% believe brands sell their data. This may reflect the attention drawn to data sharing by the European Union’s General Data Protection Regulation (GDPR). Europeans' lack of trust in most business may also explain why they are more likely to support brands that do show high purpose.
Now let’s turn to marketers. Most European marketers will tell you that their region is behind the U.S. in adoption of advanced marketing technology. European consumer perceptions of less personalization support this. The data here do show that European marketers use fewer data sources and channels for most purposes, although the figure for inputs to attribution is higher. The differences are relatively small with the significant exception that Europeans report using personalization in 20% fewer channels (4.1 vs 5.1) than U.S. marketers.
The gap is larger when we focus specifically on data integration. European marketers are much more likely to cite challenges with linking multiple data sources, more likely to see linking data as the reason to deploy a Data Management Platform, and more likely to avoid a DMP because the technology is too complex. While integration is a substantial problem for many U.S. based marketers, it’s clear the pain is greater in Europe – despite having slightly fewer data sources to integrate.
The same pattern holds for marketing technology in general. European marketers spend a slightly smaller share of their marketing budget on martech and a slightly smaller share of their martech budget on data and analytics. But while those differences are fairly small, U.S. marketers expect much higher growth in their 2019 martech budgets. This is a significant indicator of attitudes regardless of what actually happens. Similarly, European marketers show consistently but slightly lower adoption of advanced marketing systems such as DMP, cross-channel engagement, and flexible attribution models.
Looking beyond technology, we see that U.S. and European marketers share a high level of belief in personalization. But European marketers rank lower on other measures that indicate maturity. It’s particularly intriguing that European marketers are less likely than U.S. marketers to be prioritizing first party data, even though GDPR is generally assumed to make first party data more important.
In sum, the belief that European marketers are using less advanced technology than U.S. marketers appears to be correct.
Leaders vs Mainstream
What separates the most successful marketers from the rest? This data, all from the same survey, found that high performing marketing departments were twice as likely to be responsible for technical activities related to customer data: operations, governance, security, and schemas. This suggests that marketers do in fact get better results when they have more control over their customer data. By contrast, leading and mainstream departments had similar responsibility levels for traditional marketing activities such as automation rules, data acquisition, and analytics.
It’s important to qualify this message. Even among leading marketing departments, the majority do not have technical responsibilities. So clearly success is possible under other arrangements. It’s also important to recognize that marketing and IT will almost always share some responsibilities. And they should.
Other leader vs mainstream comparisons provide more insight into the challenges faced at different maturity levels. Mainstream marketers are more likely than leaders to cite disparate technology as their biggest martech challenge: this suggests that is the first hurdle to cross. Leaders, having started to knit together their systems, are likely to run into organizational barriers next. Once they resolve organizational problems, they can deliver results such as a single customer view and quantifying the benefits of personalization and real time marketing. Few mainstream marketers, still fighting technical and organizational battles, are able to accomplish these.
Some markers show much less correlation with leadership. Mainstream marketers are nearly as likely as leaders to lead customer experience initiatives and to run real time interactions in at least one channel. Note that single channel real interactions do not require unified customer data or any type of shared systems. So they are not by themselves an indication of maturity.
Customer Data Platforms
Finally, we’ll look at some information specifically related to Customer Data Platforms. The table below compares CDP selection priorities for enterprise vs mid-tier buyers. It supports the common belief that these groups have different concerns. Enterprise marketers give higher priority to data security and integrating data from many sources, including third party data. Mid-market buyers also rank security as their top concern but then look for help with internal data and for data analysis tools. These are probably problems that enterprises have already solved. One implication is that CDP vendors may find themselves specializing in one or the other type of buyer so they can optimize their systems for the different needs.
I also have several surveys that asked about CDP deployment. Answers vary greatly although the general result suggests that CDP adoption is getting close to DMP adoption. The very low figure from Heinz Marketing reflects the nature of its survey, which asked B2B marketers about tools for marketing analytics and pipeline management. The audiences for the other surveys were more representative but the figures still seem much higher than likely. The CDP Institute’s own estimate is that market penetration for CDPs at the end of 2018 was around 15%.
Note on Sources
This paper draws from surveys with different audiences, survey methods, and sample sizes. The origin of each item is indicated by a number that relates to the list of surveys below. This list provides some information about each survey, as presented in the survey report. Data from the original surveys has been processed in several ways:
• Questions have been paraphrased for brevity and clarity. • European results are averages of country results, which have been weighted in different cases by national population, sample size, or not at all. Different surveys included different countries. • Some U.S. results include data from all of North America.
Readers should be able to track down the original survey reports on the Internet. I haven't published links because links change too often to be useful.
1 Acquia, Closing the CX Gap: Customer Experience Trends Report 2019. More than 5,000 consumers and 500 marketers. 2 AdRoll, The State of Marketing Attribution, 2017. 987 respondents recruited by email and social media. Majority at director/manager level. 3 Aspect, 2017 Aspect Consumer Experience Index. Online survey with 1,000 aggregate U.S. sample and similar in Germany, Spain, United Kingdom. 4 Econsultancy, The Customer Data Imperative, 2018. 509 online survey respondents, primarily at large B2C brands. Mix of marketing, IT, and operations. 5 Edelman, 2018 Edelman Trust Barometer. 33,000_ online survey respondents across 28 countries. 6 ExchangeWire, Adoption vs Execution: How Media Agencies Across the Globe Are Making the Most of their DMP’s Capabilities, 2017. 470 agency professionals. 7 Frost & Sullivan, The Global State of Online Digital Trust, 2018. 990 survey responses. 8 Gemalto, Data Security Confidence Index, 2018. 1,050 IT decision makers from organizations with perimeter security systems. 9 GlobalWebIndex, Trends 19, 2019. 91,913 Internet users aged 16-64. 10 Harvard Business Review Analytics Services, The Age of Personalization, 2018. 625 responders from audience of Harvard Business Review readers. Primarily executive/senior management at large enterprises. 11 Heinz Marketing, State of Revenue Marketing, 2018. 241 B2B marketing executives, primarily small to mid-size companies. 12 Infosys, Endless Possibilities with Data, 2018. 1,062 senior executives from organizations with annual revenues exceeding $1 billion. 13 Ipsos+Medallia, The Customer Experience Tipping Point, 2018. 8,002 consumers in U.S., UK, France, Germany. 14 Mulesoft, Consumer Connectivity Insights 2018. 650 IT decision makers at organization with 1,000+ employees. 15 Relevancy Group, CDP Buyers Guide 2018. 406 executive marketers. 16 Salesforce Research, Fifth Edition State of Marketing 2019. 4,101 responses from full-time marketing leaders, primarily mid-size organizations. Mix of B2B and B2C. 17 Sizmek, Marketers Survey Results 2018: An Insider’s Look at Data, Walled Gardens, and Collaboration. 522 B2C brand marketers. 18 Spiceworks, 2019 State of IT, IT Marketing. 780 business technology buyers. 19 Walker Sands, State of Marketing Technology 2018. 300 marketing professionals. Primarily small to mid-size companies. 20 WE Communications, Brands in Motion 2018. Online interview of consumer survey panel totaling 11,000+ in U.S., U.K., and Germany. 21 Winterberry Group, Know Your Audience: The Evolution of Identity in a Consumer-Centric Marketplace, 2018. Online survey of more than 400 advertisers, marketers, fundraisers, publishers, technology developers and marketing service providers.
What do you think potential CDP buyers would find the most interesting stats from this?
From a buyer’s perspective, the most interesting stat may be the absolute number of local vendors available. We found 25 Europe-based CDP vendors, nearly one-third of the worldwide total. Marketers in UK, France, or The Netherlands can each pick from a half dozen vendors based in their country. This matters because markets still have considerable national differences. And I suspect that many buyers prefer to work with a local firm, especially for customer-facing functions such as personalization where knowledge of local buyers is important.
Buyers may also be interested in the market growth rate: the number of European vendors doubled, from 13 to 25, during 2018, while employment grew by 58% from 929 to 1,469. Cumulative funding grew by just 16%, suggesting that most growth is self-funded. We estimate total revenue among these vendors at €150 million in 2018. Revenue in Europe for non-European vendors is probably about equal to this amount.
Are there significant differences from country to country?
It’s a bit hard to say about country differences because there are probably quite a few firms we don’t know about. For example, I’m aware of only one German CDP, which just seems very unlikely. And although our figures show that Netherlands-based companies are especially small, the reason could simply be that we have been more exposed in The Netherlands than elsewhere and thus are more likely to learn about small companies in that market.
What about the rest of the world vs Europe?
The differences here are clearer. Seventy percent of European CDP employees work at companies with campaign-type systems, meaning they can select messages for individuals in batch campaigns or one-on-one interactions. Just 43% of employees outside of Europe work at similar firms. The domination of the European industry by campaign CDPs is its most distinctive feature. I suspect that European marketers prefer campaign systems because European marketers have fewer technical resources than their counterparts in the U.S. Campaign systems help European marketers stretch their budgets because they include a broader range of features than other types of CDPs, reducing the need for additional products.
Are there other differences beyond campaign type?
Yes. European CDP companies are much smaller and less well-funded than those outside of Europe: the average European company has 59 employees and €6 million funding, while the average company elsewhere has 99 employees and €25 million funding. Averages can be deceiving but other measures tell a similar story. For example, only one of the ten largest CDPs is based in Europe compared with five of the ten smallest. The difference is actually greater than this suggests because most of the large European CDPs are old companies – four of the six with over 100 employees were founded before 2006. By contrast, just one of the fourteen non-European vendors with over 100 employees is that old. This means that the largest European CDPs built their business doing something else – mostly as marketing services agencies -- while the largest non-European CDPs were CDPs from the start. (You’d need to look at the data in our global industry update, available here, for the non-European information.)
Are there any trends that might help to predict the future?
We assemble this data every six months so we get a good perspective on changes over time. The European CDP industry had been growing slightly faster than the rest of the world but in the recent period that was not the case: the European share of employment actually fell slightly. Another trend that had been clear was the steady shift of industry away from access vendors towards campaign vendors. But that also slowed during the most recent period and actually reversed outside of Europe, where analytics vendors gained share. This reversal suggests that different types of CDPs are finding success among different customer groups, meaning that the industry is not going to coalesce around a small number of vendors with similar features any time soon. That is different from the path that most industries take, where a dominant model and few leaders do tend to emerge fairly quickly.
Data on industry concentration points in a similar direction: the share of employment among the top five vendors has fallen steadily over the past two years, and even the share of the top 20% of vendors (a number that increases as the industry grows) fell in the most recent period.
Over all, the report shows a vibrant, innovative European CDP industry that continues to grow by taking approaches that fit the special conditions of the European marketplace. U.S.-based firms, with much higher funding and a substantial technical head start, will not leave the market any time soon. But because the non-European firms are strongest in the data access and analytics categories, we see both sets of vendors coexisting and often even being used for different purposes within the same company.
The CDP industry had a great 2018, growing by 60% and reaching $740 million revenue, according to the CDP Institute’s just-released Industry Update (click here to download). The industry added 29 vendors and 2,653 employees, slightly more than the 26 vendors and 2,233 employees added in 2017. Vendor funding increased $568 million in 2018 vs $492 million in 2017.
But the smooth growth hides some interesting twists. The report period (second half of 2018) saw the first two major acquisitions of CDP vendors: Datorama by Salesforce for reported $800 million and Treasure Data for $600 million by ARM Holdings for reported $600 million. It saw a new generation of purpose-built CDP systems for vertical industries and personalized messaging. There was a sharp drop in new CDP funding. And increased competition from non-CDP vendors is clearly on the horizon.
One thing that didn't happen was the long-expected industry consolidation. New vendors are being added more quickly than existing vendors can grow – so the industry is becoming more fragmented.
Let’s look at each of these developments in more depth.
Acquisitions. The CDP industry had already seen quite a few small acquisitions, mostly by agencies or vertical industry specialists adding customer data management to their existing product lines. But Datorama and Treasure Data were the first acquisitions of large CDP companies. They were surely the first over $100 million and possibly the first over $10 million.
Yet neither deal is a simple as a large marketing suite adding a CDP module. Salesforce is a marketing suite vendor but it purchased (and so far is selling) Datorama as a marketing performance analysis tool. This is how Datorama had positioned itself before the acquisition, even though Datorama does have a few clients using it to manage individual-level customer data in true CDP fashion. ARM Holdings, itself owned by tech conglomerate SoftBank, is a chip technology company that purchased Treasure Data to manage Internet of Things data. (Treasure Data will continue selling itself as a conventional CDP.) Yet, regardless of their intent, Salesforce and ARM are likely to find their clients using their acquisitions for CDP-style customer data management. Demand for such solutions is simply too strong to avoid that happening. And you can be sure that clever salespeople at both companies will offer the products as CDPs if that's what it takes to close a deal.
Next-Generation CDPs. Nine of the vendors added during the period were founded in 2014 or later – the first time a majority of new entrants were this young. These companies are small and lightly funded, averaging just 25 employees and $4 million. They can be seen as “CDP natives” that were designed from the start as CDP systems. By contrast, the majority of vendors entering the industry in earlier periods were older, larger, and better funded. Most began as something else and later repositioned as CDPs.
The new firms included more data access and analytics vendors than previous periods, again indicating that they were purpose-built for specific CDP applications and industries. This is the first period since reporting began that the industry share of campaign vendors decreased.
Organic Growth and Fragmentation. Most industry growth in the past two years– a remarkably constant 72%, as it happens -- has come from new vendors, not organic growth by existing vendors. One implication is that adding younger, smaller companies could reduce the industry growth rate. Whether this happens depends on how quickly the existing vendors grow. So far, employment at existing firms has increased around 10% per six-month period, which translates to a healthy but unspectacular 20% per year. One cause for optimism: younger firms have tended to grow faster than older firms, suggesting the rate might increase as more young firms appear.
Larger firms have also grown faster than smaller firms. Ordinarily this would result in higher industry concentration, but so far the entrance of new firms has more than outweighed this effect. The industry share of the five largest vendors has in fact decreased from 46% in the first report to 28% in the latest report. I expect this fragmentation to increase as more specialized CDP vendors carve out niches in particular industries, regions, and functions.
Funding. We measure funding in two ways. One looks at aggregate funding for all industry vendors; this grows as new vendors are added, regardless of when the funding took place. The other approach looks at the timing of funding events. It includes events that happened before a vendor joined the industry. The first measure, aggregate funding, has increased in rough parallel with industry vendors and employment. The second measure is more volatile and shows a sharp decrease since its peak in the second half of 2017.
The patterns of the funding have also changed over time. In 2018, little funding went to data access firms, companies founded before 2010, or the five largest companies (accounting for 25% of industry employment) even though those are still a major portion of the industry. This suggests that those firms had already acquired ample funding to support their growth.
The decline in new funding during the most recent period may mean that investors are becoming cautious about supporting new CDP vendors. Whether this slows industry growth remains to be seen: the smaller, niche CDP vendors are more able to finance their early activities from personal investment and operations. It’s likely that the more successful of these will attract additional funding in the future.
External Competition. There’s nothing in the CDP industry data to capture external competitors. But last fall saw announcements of products addressing the same needs as CDPs by Salesforce, Oracle, and an Adobe/Microsoft/SAP alliance. My take is that the Oracle and Adobe entries are architecturally similar to CDPs as we define them (“packaged software that builds a unified persistent customer database accessible by other systems”) while the Salesforce entry falls short because it doesn’t create a persistent database. None of these products is fully operational but the announcements alone will deter some buyers from purchasing alternatives. We’ve also seen many personalization and delivery system vendors beef up their customer data management capabilities in ways that make them more CDP-like. There’s no doubt that the growth of the CDP industry has captured other vendors’ attention, both as an opportunity for expansion and as a threat to their existing business. Competition from these vendors can only increase as they better understand the capabilities needed for a CDP.
We’ve also seen existing CDP vendors expand their own footprints, often by adding email delivery capabilities. These are offered as a convenience for their clients, not because the CDPs want to enter the email business. But the result is to further blur the distinction between CDPs and other systems.
The net impact of these changes on independent CDP vendors is still in doubt. Some companies will surely prefer to buy a CDP that’s a component of their existing delivery system or marketing suite. But all will become more educated about true CDP requirements and many will find that these are best met by a separate product. Pressure from buyers may also force delivery systems and suite vendors to open up their products to external connections, both to feed data to an external CDP and to read CDP data. This greater openness will make it easier to use an independent CDP and thus reduce the benefits of buying a CDP that’s built into a larger system. If analysts who predict a world of microservices plugged into central platforms via standard APIs are correct – I’m looking at you, Scott Brinker – then independent CDPs will prosper within this open future environment.
It’s been quite fascinating to watch the Customer Data Platform industry develop over the past few years. So far, we’ve seen two main trends emerge: extension of CDP product scope beyond the core of building the customer database itself and expansion into new industries. Both shed light on what we can expect to happen next.
CDP product scope has a more convoluted history than you might think. When the CDP concept was first defined, it referred to systems that built a customer database to support a specific marketing application such as predictive modeling or campaign management. It took some time for the vendors to realize that the database itself was ultimately more valuable than any one application, because the database was more central to their clients’ needs. At the same time, other people – often marketers who had found through personal experience that unified customer data was rarely available – started by build database-only CDPs, based on the same insight that the value was in the database.
The junction of these two streams is one reason that CDPs have always been so bafflingly diverse: some products started with a footprint that included applications, while others started with the database alone. Another reason is that CDPs also included third stream of vendors: tag managers whose original focus was on building connectors to ingest and distribute data.
But the nature of software products is to expand their functions. A cynic might argue the reason is that companies have developers on staff and need to keep them busy. But a more realistic explanation is that clients are always asking for new features and vendors are eager to oblige. What this means for CDPs is that even vendors who started out simply assembling data or building databases have added applications, typically starting with analytical features such as segmentation, visualization, and predictive modeling, and then moving further towards execution with message selection and experience orchestration. The step after that is message delivery – and, sure enough, we’re starting to see CDPs with email engines as well.
This raises new definitional challenges, since at some point a system that is actively executing customer experiences is clearly more than a CDP. I personally have no problem with this, having argued some time ago that we should distinguish between CDP functions, which can be part of a larger product, and CDP systems, which focus on building a CDP only. We’re seeing an increasing number of products that include CDP functions in a larger package, including offerings from Oracle and Adobe. Think of it as "CDP inside".
The trick will be for buyers to understand that whether a customer database is a stand-alone product or a component of something bigger, it’s only a CDP if it meets the definition: packaged software that builds a unified persistent customer database that is accessible to other systems. Oddly enough, “accessible to other systems” turns out to be the most critical element, because many vendors build a CDP-style database to support their own applications and don't share it with others. So, one of my major tasks in the year ahead will be to stress this point to anyone who will listen. The message will be very much along the lines of the original “Intel Inside” campaign: “insist on the real thing – accept no substitutes”. It’s a dauntingly subtle message to convey in a world where attention spans are measured in seconds. I like to think of it as a challenge.
Most early CDPs were deployed at retail and publishing companies. Financial services and travel/hospitality came next, and adoption has recently spread to B2B, healthcare, education, and telco. In itself, this progression isn’t news. But I just recently saw a presentation by CDP vendor Boxever that suggested the sequence was more than random. They pointed out that adoption came earliest in industries with the shortest, most transactional buying cycles, and then spread quite steadily to industries with longer cycles and higher product costs.
This may seem obvious in hindsight, but it’s not the only possible explanation. My previous view was retail and publishing were early adopters because they had such poor systems in place before CDPs, making the incremental benefit higher than in banking and travel, here customer data was already fairly well organized. You could also argue that retail and publishing they’re the industries under the most competitive pressure from online companies, and thus with the greatest need for customer data to deliver personalized experiences. But those explanations always felt a bit contrived and ran into the fact that retail, financial, travel, and telecommunications had always been the leading industries in customer data-driven marketing. So, it always struck me as odd that they were adopting CDP at such different rates.
On the other hand, viewing CDP utility as a function of buying cycle does make sense. Companies with quick, simple transactions have more data points and simpler purchase processes than companies with fewer, longer running, more complicated transactions. This means those companies can more easily derive benefit from a CDP through tactical applications like predictive modeling to select lists or recommend a next offer. The other industries can still benefit from unified customer data but will need deeper analytics to convert their data into value.
A corollary may be that vertical specialists will have higher success rates in these late-adopting industries because their greater complexity requires specialized applications that only industry experts can build and explain. Many of these applications with require tight integration with industry operational systems, such as ticketing in travel, call details in telecommunications, and medical records in health care. B2B might seem an exception but that’s only because its specialized systems are marketing automation and sales automation, which are very familiar to many marketing technologists.
In any event, if the correlation between CDP adoption and buying cycle complexity is valid, it's a useful tool for assessing how easily CDP vendors can penetrate new marketers. That is surely a helpful thing to have.
While industry history is interesting, the question everyone really cares about is, What happens next? That CDP vendors will continue to expand their footprint is obvious. So is growth into new industries. Less clear is whether stand-alone CDPs will continue to thrive or “CDP inside” solutions will take over.
We are in fact already seeing a movement in the “CDP inside” direction. Leaders include the big marketing clouds and narrower vendors with roots in email and Web site personalization. Acquisitions are one sign that companies are expanding their capabilities and, sure enough, last year saw CDP acquisitions including Datorama, Treasure Data, and several smaller companies (Marketing G2, Datatrics, and Audiens).
So the best bet is that the CDP market will follow the same pattern as other markets, with best-of-breed products slowly replaced by integrated solutions, starting in the mid-market and working up into larger enterprises. At the very high end, CDPs with advanced data management technology may survive and even grow in the short term, but they’ll ultimately be pushed into a corner with dwindling market share. Some other vendors may carve out niches in data connectivity, identity resolution, or other specialized functions within the CDP stack. At the other extreme, vendors with broad functions might find success as integrated solutions, especially if they are specialists in a particular industry. The hardest position to maintain will be a data-focused CDP serving mid-size companies: those firms will face increasingly compelling competition from broad-scope vendors who offer a CDP as part of a larger product.
Whether this is good or bad news depends on where you sit. Late-to-market CDP vendors, especially data-focused firms lacking special technology, may find the window of opportunity has already shut. Companies with broad functions that are adding CDP features shouldn’t have a problem, although they may need to talk more about their applications and less about their internal CDP.
Buyers, on the other hand, stand to benefit from a wider range of systems that offer the core CDP benefits of unified, shareable customer data. What the CDP industry has accomplished is to establish the need for a unified, open customer database as a central component of every company’s marketing technology architecture. Marketers – and others who use customer data – must insist on solutions that fully meet that need in terms of ingesting data from all sources, retaining full detail, and making thr results accessible to all external systems. Ensuring that solutions meet those conditions isn't easy: it’s hard for even experienced technology buyers to understand what different systems really do. Yet buyers have no choice but to be thorough in their evaluation processes: at most companies today, a proper CDP is a foundation for business success.
I finally caught up with Oracle for a briefing on the CX Unity product they announced in October. Although it was clear at the time that CX Unity offered some version of unified customer data, it was hard to understand exactly what was being delivered. The picture is now much clearer. Here are straight answers to important questions:
It’s a persistent database. CX Unity will ingest all types of data – structured, semi-structured, and unstructured – from Oracle’s own CX systems via prebuilt connectors and from external systems via APIs, batch imports, or Oracle’s integration cloud. It will store these in well-defined structures defined by marketing operations or similar lightly-technical users. The structures will include both raw data and derived variables such as predictive model scores. Oracle plans to release a dozen industry-specific data schemas including B2B and B2C verticals.
It does identity resolution. CX Unity will support deterministic matching for known relationships between customer identifiers and will maintain a persistent ID over time. It will link to the Oracle Identity Graph in the Oracle Data Cloud for probabilistic matches using third party data.
It activates data in near real time. CX Unity can ingest data in real time but it takes 15 to 20 minutes or longer to standardize, match, run models, and place it in accessible formats such as data cubes. Oracle expects that real-time interactions and triggers will run outside of CX Unity.
It shares data with all other systems. Oracle has built connectors to expose CX Unity data within its own customer-facing CX Cloud systems. APIs are available to publish data to other systems but it’s up to partners and clients to use them.
It integrates machine learning. CX Unity includes machine learning for predictive models and recommendations. Results are exposed to customer-facing systems. This capability seems to be what Oracle has in mind when it contrasts CX Unity with other customer data management solutions that it calls merely “database centric”. It’s not live yet. The B2C customer segmentation features of CX Unity are available now. The full system is slated to be available in mid-2019.
These answers mean that CX Unity meets the definition of a Customer Data Platform: packaged software that creates a unified persistent customer database accessible by other systems. The machine learning and recommendation features would put it in the class of “personalization” CDPs I defined earlier this month. This is a sharp contrast with the CDP alternatives from Oracle’s main marketing cloud competitors: Customer 360 from Salesforce (no persistent database) and Open Data Initiative from Adobe, Microsoft and SAP (more of a standard than a packaged system).
It's likely that CX Unity will be bought mostly by current Oracle CX customers, although Oracle would doubtless be happy to sell it elsewhere. But even if CX Unity sales are limited, its feature list offers a template for buyers to measure other systems against. That will create a broader understanding of what belong in a customer management system and make it more likely that buyers will get a CDP that truly meet their needs. So its release is a welcome development – especially as Oracle finds ways to present it effectively.
“If you can fake sincerity, you’ve got it made” runs the old joke. The irony-impaired managers of the Association of National Advertisers (ANA) seem to have taken this as serious advice, last week announcing creation of a “Center for Brand Purpose” that will help companies publicize their social purpose. Confirming the worst stereotypes of marketers as cynical hucksters, the press release promotes its mission with the argument that “purpose-led brands grow two to three times faster than their competitors.”
The ANA’s grasp of causation may be no stronger than its sense of morality, but there’s no question it's in tune with the marketing herd. Issue-based marketing is hot. Another announcement last week illustrates the point: a new program from a coalition of 2,600 socially-responsible “Certified B Corporations” such as Ben & Jerry’s aims to convince consumers to buy from companies that share their values.
Most of the B Corps have a legitimate history of activism. But the broader interest in taking social positions is intriguing precisely because the B Corps have been exceptions to the conventional wisdom that businesses should avoid controversial issues. Studies on the topic show mixed results1 and that most people care more about practical matters2. So why the sudden change?
But I think we can legitimately cite broader trends that have made marketers receptive to the shift. Fox News has demonstrated over the past twenty years that there’s a mass market for partisan bias. The growth of right-wing extremism has led to push-back in support of fairness, reason, and rule of law. The recent election results can be read as a majority rejection of extremism, although other interpretations are possible. If there is indeed an emerging consensus that American ideals are under threat, it’s now safer for companies to promote human rights, the environment, and fair employment practices: positions with broad public support despite attacks from the right.
There are other, more parochial reasons for some companies to take strong policy positions. Companies whose customers are concentrated on one or the other side of the urban/rural divide may benefit from polarizing choices: pro-Kaepernick for Nike, anti-abortion for Hobby Lobby. Industries with bad reputations may aim to change public opinion: oil companies touting their environmental concerns are a long-standing example (although this rarely extends to support for policies that would limit their profits).
Tech companies are particularly susceptible to reputational damage because they need to recruit tech workers, who skew young, educated, urban, and immigrant. The best of those workers have many employment options and want to work at firms that agree with their values. Again, the problem is most severe for Facebook, whose own employees are increasingly concerned that it is doing more harm than good. But employee protests at Google , Amazon and Microsoft tell a similar story. The recent walkout by 20,000 Google workers over sexual harassment is one more example – and was followed by changes in policies at other tech-driven firms including eBay, Airbnb, and Facebook itself. On the other side of the ledger, Apple has made privacy protection a core part of its own brand, both calling for regulation and resisting government requests to share data. Not coincidentally, Apple’s business isn’t as dependent on consumer data as many of its competitors.
Which brings us back to the original topic. If brands are taking political positions because that’s a good marketing tactic, the insincerity seems reprehensible. It also opens the door to brands supporting socially harmful positions if those are the most popular. At best, expectations should be limited: no one expects a brand to support a policy that hurts its own business, and in fact we’re used to brands advocating policies that favor them. So any position taken by a business must be viewed with skepticism.3
On the other hand, businesses do have a fundamental self-interest in promoting healthy social, political and physical environments. Advocating policies that protect those environments is a perfectly legitimate activity. Publicizing that advocacy is part of the advocacy itself. That it’s now seen as good marketing isn’t new and it isn’t bad: it’s just how things are at this particular moment.
2 InMoment found 9% of consumers care mostly about brand purpose while 39% care mostly about functionality. The remainder care about both. Waggener Edstrom reports that 55% of consumers say brands can earn their trust by delivering what they promise while fewer than 10% said trust is earned by supporting shared values.
3 Not to mention that brands have been known to attack legitimate research to support their preferred positions, to secretly fund supporters and attacks on opponents, and to say one thing while doing another.
I spend much of my time these days trying to explain Customer Data Platforms to people who suspect a CDP could help them but lack clear understanding of exactly what a CDP can do. At the end of our encounter they’re often frustrated: a simple definition of CDP still eludes them. The fundamental reason is that CDPs are not simple: the industry has rapidly evolved numerous subspecies of CDPs that are as different from each other as the different kinds of dinosaurs. Just as the popular understanding of dinosaurs – big, cold-blooded and extinct – has little to do with the scientific definition, a meaningful understanding of CDPs often has little to do with what people initially expect.
Let’s start with the big picture. I’ve for years divided customer management systems into three broad categories, which are best seen as layers in a unified architecture. Frequent readers of this blog can feel free to recite them along with me (throwing rice at the screen is optional): data, decisions, and delivery.
Refining this notion just a bit:, the data layer includes systems that create customer data and systems that store it in a unified customer database. Decision systems include several categories such as marketing planning and content management, but the most relevant here are analytics, including segmentation and predictive models, and personalization*, which selects the best message for each individual. The delivery layer holds both systems that send outbound messages such as email and advertising and interactive systems such as Web sites and call centers. An important point is it’s hard to do a good job of delivering messages, so delivery systems are large, complex products. Picking the right message is just one of many features and often developers’ main concern.
A complete architecture has entries in each of these five categories. But many companies have multiple source and delivery systems that are disconnected: these are the infamous silos. The core technology challenge facing today’s marketers can be viewed as connecting these silos by adding the customer database, analytics, and personalization components that sit in between.
By definition, the CDP fills the Customer Database gap. Some CDPs do only that – I will uncreatively label them as “Data CDPs”.
I’ll also take a slight detour to remind you that the customer database must be persistent – that is, it has to copy data from other systems and store it. This is necessary to track customers over time, since the source systems often don’t retain old identifiers (such as a previous mailing or email address) or, if they do keep them, don’t retain linkages between old identifiers and new ones. There’s also lots of other data that source systems discard once they have no immediate need for it, such as location, loyalty status or life-to-date purchases at the time of a transaction. Marketing and analytical systems may need these and it’s often not possible or practical to reconstruct them from what the source systems retained. This is especially true in situations where the data must be accessed instantly to support real time processes.
But I digress. Back to our Data CDP, which obviously leaves the additional gaps of analytics and personalization. Why wouldn’t a CDP fill those as well? One answer is that some CDPs do fill them: we’ll label CDPs with a customer database plus customer analytics as “Analytics CDPs” and those with a customer Database, analytics, and personalization as “Personalization CDPs”, again winning no prizes for creativity. A second answer is that some companies already have chosen tools they want for analytics or personalization. Like message delivery, those are complicated tasks that can easily be the sole focus of a “best of breed” product or products.
This variety of CDPs also addresses another question that some find perplexing: why one company might purchase more than one CDP. As you’d expect, different CDPs are better at some things than others. In particular, some systems are especially good at database building while others are good at analytics or personalization. It often depends on the origins of the product. The result is that a company might buy one CDP for its database features and have it send a unified feed into a second CDP for analytics and/or personalization. There are some extra cost and effort involved but in some situations they're worth it.
Are you still with me? I’ve presented three different types of CDPs but hope the differences in what they do and which you’d want are fairly clear.** Now comes the advanced course: other systems that either call themselves CDPs or offer CDP alternatives.
These fall into many categories but can all be mapped to the same set of five capabilities. Let’s start with Marketing Suites, by which I mean delivery systems that have expanded backwards to include a customer database, analytics and personalization. Many email vendors have done this and it’s increasingly common among Web personalization and mobile app marketing products. In most cases, these vendors now deliver across multiple channels. Adobe Experience Cloud also fits in here.
To qualify as a CDP, these systems would need to ingest data from all sources, maintain full input detail, and share the results with other systems. Many don’t, some do. We could easily add another CDP category to cover them – “Marketing Suite CDP” would work just fine. But this probably stretches the definition of CDP past the breaking point. For CDP to have any meaning, it must describe a system whose primary purpose is to build a persistent, sharable customer database. The primary purpose of delivery systems is delivery, something that’s hard to do well and will remain the primary focus of vendors who do it. So rather than over-extend the definition of CDP, let’s think of these as systems that include a customer database as one of their features.
We also have some easier cases to consider, which are systems that provide customer analytics and personalization but don’t build a unified customer database. Some of these also provide delivery functions – examples include marketing automation, CRM, and ecommerce platforms. Others don’t do delivery; we can label them as Orchestration. In both cases, the lack of a unified, sharable customer database makes it clear that they’re not CDPs. Complementing them with a CDP is an obvious option. So not much confusion there, at least.
Finally, we come to the Customer Experience Clouds: collections of systems that promise a complete set of customer-facing systems. Oracle and Salesforce are high on this list. Both of those vendors have recently introduced solutions (CX Unity and Customer 360) that are positioned as providing a unified customer view. It’s clear that Salesforce does this by accessing source data in real time, rather than creating a unified, persistent database. Oracle has been vague on the details but it looks like they take a similar approach. In other words, the reality for those systems shows a gap where the persistent customer database should be. Again, this makes CDPs an excellent complement, although the vendors might disagree.
So, there you have it. I won’t claim the answers are simple but do hope they’re a little more clear. All CDPs build a unified, persistent, sharable customer database. Some add analytics and personalization. If they extend to delivery, they're not a CDP. Systems that aren’t CDPs may also build a customer database but you have to look closely to ensure it’s unified, persistent and shareable. Often a CDP will complement other systems; in some cases, it might replace them.
Still disappointed? I am genuinely sorry. But if it helps bear this in mind: while simple answers are nice, correct answers—which in this case means getting a solution that fits your needs – are what matter most.
_______________________________________________ *I usually call this ‘engagement’ but think ‘personalization’ will be easier to understand in today’s context. For the record, I’m specifically referring here to selecting the best message on an individual-by-individual basis, which isn’t necessarily implied by ‘personalization’.
**If you want to know which CDP vendors fit into each category, the CDP Institute’s free Vendor Comparison report covers these and other differentiators. Products without automated predictive modeling can be considered Data CDPs; those having automated predictive but lacking multi-step campaigns could be considered Analytics CDPs; those with multi-step campaigns could be considered Personalization CDPs. There are many other nuances that could be relevant to your particular situation: the report lists 27 differentiators in all.
Recent discussions with Customer Data Platform buyers and vendors have repeatedly circled around a small set of questions:
what are the use cases for CDP? (This really means, when should you use a CDP and when should you use something else?)
what are the capabilities of a CDP? (This really means, what are the unique features I’ll find only in a CDP? It might also mean, what features do all CDPs share and which are found in some but not others?)
which CDPs have which capabilities? (This really means, which CDPs match my requirements?)
can someone create a standardized CDP Request for Proposal? (This comes from vendors who are now receiving many poorly written CDP RFPs.)
These questions are intertwined: use cases determine the capabilities users need; requirements are the heart of an RFP, and finding which vendors have which capabilities is the goal of vendor selection. These connections suggest the questions could all be answered as part of one (complicated) solution. This might involve:
1. defining a set of common CDP use cases 2. identifying the CDP capabilities required to support each use case 3. identifying the capabilities available in specific CDPs 4. having buyers specify which use cases they want to support 5. auto-generating an RFP that lists the requirements for the buyer’s use cases 6. creating a list of vendors whose capabilities match those requirements
What’s interesting is that steps 1-3 describe information that could be assembled once and used by all buyers, while steps 5 and 6 are purely mechanical. So only step 4 (picking use cases) requires direct buyer input. This means the whole process could be made quite easy.
(Actually, there’s one more bit of buyer input, which is to specify which capabilities they already have available in existing systems. Those capabilities can then be excluded from the RFP requirements. The capabilities list could also be extended to non-CDP capabilities, since most use cases will involve other systems that could have their own gaps. These nuances don’t change the basic process.)
As a sanity check, I’ve built a small Proof of Concept for this approach using an Excel spreadsheet. I'm happy to say it works quite nicely. I'll share a simplified version here to illustrate how it works. In particular, I'll show just a few capabilities, use cases, and (anonymous) vendors. .
We’ll start with the static data.
The columns are:
Capability: a system capability.
Description: a description of the capability. This can both help users understand what it is and be a requirement in the resulting RFP. Or, we could create separate RFP language for each capability. This could go into more detail about the required features.
CDP Feature: indicates whether the capability would be found in at least some CDPs. The CDP RFP can ignore features that aren't part of the CDP, but it's still important to identify them because they could create a gap that makes the use case impossible. For example, consider the first row in the sample table, whether the Web system can accept external input. This isn't a CDP feature but it's needed to deliver the use case for Real time web interactions.
Use Cases: shows which capabilities are needed for which use case. For items that relate to a specific channel, each channel would be a separate use case. In the sample table, Single Source Access is specifically related to the Point of Sale channel while Real Time Interactions are specifically related to Web
Vendor Capabilities: these indicate whether a particular vendor provides a particular capability.
The second table looks at the items that depend on user input. The only direct user inputs are to choose which use cases apply (not shown here) and to indicate which capabilities already exist in current systems. All other items are derived from those inputs and the static data.
The columns are:
Nbr Use Cases Needing: this shows how many use cases require this capability. It’s the sum of the capability values for the selected use cases.
Already Have: this is the user’s input, showing which of the required capabilities are already available. In the sample table, the last row (site tag) is an existing capability. Since it exists, you can leave it out of the RFP.
Nbr Gaps: the number of use cases that need the capability, excluding capabilities that are already available. These are gaps. Using the number of cases, rather than a simple 1 or 0, provides some sense of how important it is to fill each gap.
Nbr CDP Gaps: the number of gap use cases that might be enabled by a CDP. The first row iIn the example, Web – accept input (ability of a Web site to accept external input) isn’t a CDP attribute, so this value is set to zero.
Gaps Filled by Vendor: the number of CDP Gaps filled by each vendor, based on the vendor capabilities. A total at the bottom of each column shows the sum for all capabilities for each vendor. This gives a rough indicator of which vendors are the best fit for a particular user.
The main outputs of this process would be:
List of gaps, prioritized by how many use cases each gap is blocking and divided into gaps that a CDP could address and gaps that need to be addressed by other systems.
List of CDP requirements, which is easily be transformed into an RFP. A complete RFP would have additional questions such as vendor background and pricing. But these are pretty much the same for all companies so they can be part of a standard template. The only other input needed from the buyer is information about her own company and goals. And even some goal information is implicit in the use cases selected.
List of CDP vendors to consider, including which vendors fill which gaps and which have the best over-all fit (i.e., fill the most gaps). This depends on having complete and accurate vendor information and will be a sensitive topic with vendors who hate to be excluded from consideration before they can talk to a potential buyer. So it's something we might not do right away. But it’s good to know it’s an option.
Beyond the Basics
We could further refine the methodology by assigning weights to different use cases, to capabilities within each use case, to existing capabilities, and to vendor capabilities. This would give a much more nuanced representation of real-world complexity. Most of this could be done within the existing framework by assigning fractions rather than ones and zeros to the tables shown above. I’m not sure how much added value users would get from the additional work, in particular given how uncertain many of the answers would be.
We could also write some rules to make general observations and recommendations based on the inputs, such as what to prioritize. We could even add a few more relevant questions to better assess the resources available to each user and further refine the recommendations. That would also be pretty easy and we could easily expand the outputs over time.
But first things first. While I think I’ve solved the problem conceptually, the real work is just beginning. We need to refine the capability categories, create proper RFP language for each category, define an adequate body of use cases, map each use case to the required capabilities, create the RFP template, and research individual vendor capabilities. I’ll probably hold off on the last item because of the work involved and the potential impact of any errors.
Of course, we can refine all these items over time. The biggest initial challenge is transforming my Excel sheet into a functioning Web application. Any decent survey tool could gather the required input but I’m not aware of one that can do the subsequent processing and results presentation. A more suitable class of system would be the interactive content products used to generate quotes and self-assessments. There are lots of these and it will be a big project to sort through them. We’ll also be constrained by cost: anything over $100 a month will be a stretch. If anybody reading this has suggestions, please send me an .
In the meantime, I’ll continue working with CDP Institute Sponsors and others to refine the categories, use cases, and other components. Again, anyone who wants to help out is welcome to participate.
This is a big project. But it directly addresses several of the key challenges facing CDP users today. I look forward to moving ahead.