You don’t need an army of data scientists to generate predictive models from your data to translate this intelligence into business value.
Every day, it seems like a new software company emerges with an AI-based offering, or a new white paper on the subject surfaces in your LinkedIn feed. You know it’s big, you know it’s transformative. What you might not know, though, is how you can actually use AI to build better relationships with your customers, improve your product experiences, and grow your business.
How AI Enhances the Way Companies Leverage Customer Data
Collecting high-quality first-party data across all of your digital touchpoints and tying this data into a unified view of your customers is the foundation of any successful growth, engagement or optimization strategy. But today’s consumers have such high expectations for personalization that in order to stand out among competing brands, it’s no longer enough to base personalization decisions on historical data alone. Teams need to engage with customers based on what they’re likely to do in the future.
This is where AI can transform a marketing team’s entire customer engagement strategy. AI adds a layer of intelligence and insight on top of what your data can explicitly tell you about your customers, and gives you the ability to execute use cases with your data that would have been impossible using static information.
Examples of AI-powered data activation use cases include:
- Prescriptive churn reduction: Predict those who are likely to churn and intervene before they do so.
- Conversion predictions: Focus your ad spend on those with a high likelihood of converting.
- In-app recommendations: Show your customers products with a high likelihood of resulting in purchases.
- Dynamic decisioning: Use propensity scoring to save offers for the customers they will impact the most.
What Prevents Growth Teams From Adopting AI?
Given the advantages that AI-powered personalization has over more traditional usages of customer data, one might wonder why every brand in the world is not currently leveraging AI insights in every campaign they run and decision they make. There are many several challenges that often stop marketing teams from getting started with AI.
Lack of Data Science Resources
Data scientists are highly sought after and hard to come by. Maintaining a team of these folks on staff may not be feasible for many small- to mid-sized companies. Even at larger organizations that do have these dedicated resources, the turnaround time for these teams to deliver on requests for predictive insights is often too long for marketing and product teams to leverage in real world use cases.
AI Insights Stay Locked in a Single Platform
Many marketing automation vendors on the market offer some form of AI insight-generating capability. While this may help marketing teams engage more intelligently with their customers on that platform, there are drawbacks to adopting AI downstream in the data stack. First, the insights this tool generates will be limited to the data that this one system receives, which will likely not reflect a complete view of the customer. Also, as far as AI insights go, what happens in downstream tools stays in downstream tools — these predictions won’t have any utility in other tools for data activation, orchestration, or analysis.
As is the case with any other customer data set, it is growth, marketing and product teams that are the end users of AI-powered insights. This being the case, the AI solution that a brand adopts should not have too high a technical bar as to preclude go-to-market teams from being able to easily and flexibly leverage it.
Lack of Quality Data
AI models are like classic cars that require high-octane fuel to run properly. The accuracy of the predictions these models generate depends on that of the data they ingest, which is why the only gas you should ever pump into your AI model is first-party data collected directly from owned digital touchpoints.
No 360 Customer Profiles
In order to generate recommendations based on a complete view of your users, AI systems require unified customer profiles, not disparate and unresolved events collected from various channels. Many off-the-rack AI solutions skip this requirement, only track activity occurring on a particular device, and calculate insights for that channel only. While convenient, this method doesn’t reflect a customer’s true history of interaction with your brand across your website, mobile apps, stores and support channels, and therefore can lead to incomplete insights.
Why Adopt a CDP for AI?
A CDP lays down a foundation of data infrastructure consisting of three key capabilities — collecting high-quality, first-party data across channels, unifying this data into a 360-view of the customer and forwarding these profiles to downstream tools for activation. By providing these capabilities, a CDP becomes the nexus of your entire marketing stack, and generating intelligent attributes with aCDP enables you to infuse your entire data ecosystem with this enrichment layer.
Intelligent attributes that live on customer profiles in your CDP can seamlessly move to tools for engagement, analytics, attribution, advertising, and back again in bi-directional data flows. Your predictive insights and recommendations become much more versatile and powerful resources than if they were siloed in a single tool in your marketing stack.
Additionally, when you use aCDP to generate predictive insights based on unified customer data, you end up with predictions based on a complete picture of your customers. Insights generated within a single downstream data activation tool, however, will be based only on the filtered event and behavioral data that this single tool has access to. This stifles the potential of the insights and limits your growth teams to using them in a single channel of activation, placing omni-channel AI-powered personalization out of reach.
With a CDP, you can derive more value from your customer data, and maximize your ROI on data-driven initiatives across your organization.
A longer version of this article previously appeared on mparticle.com.