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Brands today need more than a deep understanding of their customers—they need a way to leverage that understanding and turn it into growth. This has become increasingly challenging in the last year as consumer behaviors have undergone rapid change. Companies have numerous ways to gather first-party customer data, from their websites and mobile apps to customer transaction histories and loyalty programs. But getting a single view of the customer from these fragmented sources is difficult without a way to bring all of their disparate data and insights together. This is why many brands are turning to customer data platforms, or CDPs.
How customer data platforms benefit marketers and data science teams
A CDP is a platform that companies use to unify and centralize their customer data. Marketers and data scientists can use them to correlate dozens of different data types, including behavioral, transactional and demographic data, and use it to get a 360-degree view, or single view, of their customer. CDPs generate a profile of each customer that includes information like their purchase history, browsing history, loyalty status, attributes, location, preferences and even predictive scores like expected customer lifetime value.
As much as customer data platforms are used to understand individual customers, they’re also used to clean up data from unstructured repositories. Unlike data lakes, these platforms organize and correlate data for users, reducing or eliminating the manual effort needed to arrive at insights.
However, companies also invest their MarTech dollars into CDPs in an effort to make their data actionable, and this is where the issues with them arise.
The marketing execution gap in customer data platforms
The ultimate goal of data gathering is to use customer insights to make messaging and offers more relevant to customers, and therefore able to generate greater customer engagement and revenue. But customer data platforms don’t really have the right technology to deliver on this goal because they were created to solve the problems of data scientists, not of marketers.
Most CDPs were not built to effectively reach customer touchpoints. They can provide some personalization, such as email tokens, to external systems of engagement, but they are unable to execute marketing offer campaigns on their own. This leaves a gap between customer insights and the customers they’re meant to engage.
Marketers have to address this gap by taking the insights from their CDP and manually building offers around them, which is time consuming and labor intensive. If they then want to send those offers to every customer touchpoint, they must set up their CDP to send customer data to each system of engagement they use, such as their email marketing platform, mobile app, content management system, and social media management system.
Once all of that is done, there is no easy way to track individual offer performance across all of these separate systems. This leaves marketers sending the same handful of offers quarter after quarter, because there’s no way for them to experiment and optimize their offers without a great deal of manual work.
CDPs are also not designed to optimize the customer experience. Customer insights should lead to customers receiving content and experiences that are highly relevant to them, including highly personalized offers, on the channels that they utilize the most. But in reality, CDPs have no way to deliver on this promise.
Bridge the gap: pair your CDP with an offer optimization platform
So what will it take to connect insights from customer data platforms to marketing channels and the resulting customer experiences? To achieve this, marketers need to add one more item to their MarTech checklist: an offer optimization platform. Together, the two platforms can help marketers make the most of their customer data, customer experiences and marketing resources.
What is an offer optimization platform?
An offer optimization platform is a technology designed to help marketers run, scale and improve their marketing offers. Offer optimization platforms pick up where CDPs leave off by:
- Creating offers based on customer data
- Targeting offers to the right customers at the ideal point in their journey
- Deploying offers across marketing channels
- Tracking and measuring the results of individual offers
- Optimizing the next round of offers based on those results
Critically, an offer optimization platform can complete the above process in a matter of days, rather than the months it would take for a marketing team to execute it manually. This allows marketers to quickly run experiments, learn from customer behaviors, and leverage those behaviors or motivate new ones—they can also scale to an infinite number of offers depending on their needs. Importantly, an offer optimization platform makes CDPs more powerful, because the customer response to each offer feeds back into CDP, continually updating customer profiles. To sum it up, an offer optimization platform makes the CDP’s data actionable—without requiring action from marketers.
Example: How CDPs work with and without an offer optimization platform
To examine how the relationship between these two platforms works, let’s take a look at a marketing scenario common to many brands. Say a customer signs up for a loyalty program for the first time, and marketing wants to incentivize them to download and use their loyalty mobile app. Here are two ways this journey can unfold:
Journey 1: With a CDP Alone
- The CDP provides insights on the customer
- Marketing and/or data science team place customer in a segment based on those insights
- Marketing manually creates a reward offer for the segment to incentivize app download
- Marketing manually enters the offer into their email service provider (ESP)
- CDP sends segment data to the ESP
- Marketing sets ESP to distribute offer to segment, including customer
- customer downloads loyalty app, but the ESP cannot track downloads
- Marketing must use separate offer fulfillment software to verify completion and fulfill reward
- Offer results are not tracked, so they cannot optimize CDP customer profiles or future offers
As you can see, this process requires a lot of manual work from marketing. And after performing all of that work, there is no way to track the results of each offer, or how well the offer performed with specific customers. This means marketers can’t use their learnings to improve their next round of offers, and the CDP can’t use the data to make its customer profiles more accurate. Compare this to how the process works when on offer optimization platform is added:
Journey 2: With a CDP + Offer Optimization Platform
- CDP sends all customer data and insights to offer optimization platform
- Marketer sets goal for offer campaign - in this case, app onboarding
- Marketer can target offer by segment or any individual customer attribute
- Offer optimization platform automatically generates offer and distributes it to marketing channels
- Customer downloads loyalty app
- Offer optimization platform automatically tracks download and fulfills incentive
- Offer optimization platform tracks offer results and measures it back to KPIs
- CDP customer profile is updated with offer behavioral data
- Next round of offers is optimized based on offers from this round
Note how this journey contains far fewer tasks for marketing to perform manually, as well as much more automation. It also allows the data from the offer campaign to funnel back into both the CDP and offer optimization platform. This helps to keep data models accurate and offers relevant to customers.
Top 3 advantages of pairing a CDP with an offer optimization platform
1. Seamless, automatic offer execution across all channels
Whether you’re gathering insights from your CDP, your data science teams or both, you’ll have to determine how to turn those insights into marketing actions like offer campaigns. Then you’ll need to deploy your offers on each marketing channel you use. This manual process is too time consuming for an era where marketers must pivot quickly around changing customer behaviors.
An offer optimization platform provides automated, multichannel offer execution. This will allow you to run small experiments, learn from them, and scale as they learn, helping you quickly leverage changing customer behaviors.
2. Tracking and measurement capabilities reveal offer ROI
As we described, CDPs are not able to track or report the results of individual offers, which makes it hard to tell which tactics are and aren’t working with a CDP alone. But, adding an offer optimization platform allows you to track the results and outcomes of each offer you test. You can measure back to KPIs, like ROI per offer, in order to identify which offers to scale.
3. Greater relevance that generates greater customer engagement
Offer optimization platforms are designed to make your offers more relevant to customers, so they have more targeting and personalization capabilities than a CDP alone. With an offer optimization platform, you can dynamically target by any customer attribute and fine-tune your offers to improve business outcomes. You can also personalize any aspect of your offer, including by creative, action required, timing and duration, spend threshold and reward — even creating an infinite number of offer variations at scale without additional resources.
As the platform learns about customers, it can even adjust these parameters to personalize offers at the individual level, and deliver them at the right time in each customer’s journey. This means that your offers will become more relevant, engaging and motivating to customers over time.
CDPs make customer data more useful. But they need help to make it actionable.
If you have no way to unlock the insights in your data, adding a CDP in your MarTech stack is a good first step to take. But turning those insights into campaigns and offers isn’t easy, which is why most marketers today aren’t doing it. By incorporating an offer optimization platform, you can make that process automatic and run offer campaigns in days rather than months—that’s 10x faster marketing execution speed. You’ll be able to run quick experiments, and update your customer profiles and data models according to the results, effectively turbocharging your CDP.
Learn more by reading our new white paper, Bridging the Gap Between Data Science and Marketing, or contact us for a demo.