Enrich your CDP and personalization strategy with a decisioning engine
Meet consumers’ needs and boost your personalization strategy
Marketing automation tools and Customer Data Platforms (CDPs) have accelerated the introduction of personalized marketing. Marketeers have more insight into customer behavior than ever before and can create personalized content fully automatically. However, identifying patterns in the customer journey on a large scale and creating 1-to-1 content sometimes requires more. An AI decisioning engine can help here. Erik (Squeezely) and Sander (Building Blocks) explain how.
The modern consumer is demanding. They expect companies to know who they are, what they want and when they want it. Research by organizational consultancy firm McKinsey & Company shows that as much as 71% of consumers expect companies to engage in personalized interactions with their customers. 76% even say they get frustrated when these personalized interactions don’t exist.
The consumer is always right
In response to discerning consumer attitudes, online retailers have invested heavily in CDPs and marketing automation in recent years to target customers more personally. A CDP offers greater insight into customers, how they behave and what they are sensitive to. This works as follows:
“A CDP is a central storage point in which customer data is collected, enriched and combined,” says Erik, Marketing Manager at Squeezely.
“It builds customer profiles with information for their personal customer journey. This includes factors such as their interests, the marketing channels they use, and when they buy. This information helps you determine, for example, how and when to entice them to make a repeat purchase.”
This could be an automated cart abandonment email to remind them of their shopping cart, or a pop-up on the website that tells them how many items are still in stock or when they placed their most recent order. Marketing automation means this can be created fully automatically.
Enrich your CDP and personalization strategy with a decisioning engine
With a CDP, you take the first impactful steps in personalization. But if you want to identify deep-seated patterns in the customer journey for your organization and create 1-to-1 content on a large scale, it’s a good idea to enrich your CDP with a Decisioning Engine (DE). This is the way to really get the most out of your CDP and take your personalization strategy to the next level.
Less human, more machine: the decisioning engine in a nutshell
There are two main reasons why you can make even more impact on the personalization front with a CDP combined with a DE. The first has to do with human influence, the second with time.
“A marketeer using a CDP for personalization purposes is largely in control. After all, you decide which customer journeys to build for each segment based on the insights,” says Sander, Head of Marketing at Building Blocks.
“A DE like Building Blocks does the same thing, but with a self-learning algorithm. It determines a hyper-personalized next best action for each individual customer based on data from the CDP, combined with data from other systems. In other words, what message is sent out through what channel at what time. It’s always optimized to the business goals of the organization and what is needed to achieve conversion of the individual customer again. It limits subjective, human influence.”
This brings us to the second argument, because the work that a DE takes off a marketeer’s hands saves them a lot of time.
“As an organization with a small product range and a small target audience, drawing out marketing flows manually with a CDP is perfectly doable. However, it becomes more complicated when you have dozens of products and target groups, let alone hundreds or even thousands. This is where a decisioning engine really makes a difference if you want to personalize at the individual level,” says Erik.
Double personalization, double profit
The more complex the offering and the broader the target group, the greater the impact you can make with a DE.
“You make the biggest gains if you can personalize in both areas,”
says Sander. Take a vacation provider as an example. There are numerous factors that influence booking a vacation, such as the time of the vacation, the group travelling, the transportation, the budget, and so on.
“You used to email the entire customer base one last-minute offer hoping someone would bite,” Sander says.
“With a CDP combined with a decisioning engine, you can present all individual customers with a 1-to-1 personalized offer and see exactly what works and what doesn’t, without even having to manually create a mailing.”
Room for the creative marketeer
Does that leave any work for the marketeer, you may ask?
Sander: “It may sound counterintuitive, but when you work with a decisioning engine there is actually more room for creative ideas.”
Erik agrees: “A marketeer also needs to do less boring work in analytics to reveal insights, since the CDP and the DE do that for you. This allows you to focus on content.”
An important part of this content is retention, an area which Sander believes should be given more attention.
“Many businesses spend a considerable amount on advertising, but leave the back door open in the process. They are constantly trying to bring in new customers without further investment. With a decisioning engine, you get to know your customers much better, which also means you can serve them more personally. This improves customer relations and reduces the likelihood of them going to a competitor. As a result, customer lifetime value ultimately skyrockets.”
Personalization as part of the business strategy
These developments, by the way, do not mean that personalization is a one-man marketing show.
“Thanks to the decisioning engine, you can make marketing as a whole, and personalization in particular, part of your business strategy. In fact, optimizations are always made based on a particular goal, not just the customer journey. Since you can easily predict the consequence of a particular action with a decisioning engine, you can also better estimate the company-wide return.”
Sander also sees this with clients working with Building Blocks’ DE and Squeezely’s CDP.
“Whereas with a CDP you could already combine data from different online sources, a decisioning engine helps you link this to offline channels. By linking the various systems, you can make the customer journey a more integrated part of your business. Even customer service then knows which personalized emails a customer receives and which flyer is on the doormat. A decisioning engine also creates much more synergy between the different disciplines.”
The end of the interpretation era
A decisioning engine offers a solution for online retailers looking to take the next step in personalization. Erik:
“Where a CDP provides the insights, with a DE you execute actions. That very combination makes it incredibly interesting and allows you to make connections for which you don’t have the computing power yourself.”
Those who think you have to play out a CDP before you can start with a DE are incorrect.
“They are two processes that run in parallel,” according to Sander. “As with Squeezely, it’s wise to start small with a decisioning engine, such as with 1-to-1 personalization of email campaigns. If this goes well, scale up further to other channels. That’s how you keep tweaking and developing.”
Tweaking and developing not only brings creativity back into the marketing profession, but also helps you to fully utilize the potential of a CDP.
Sander: “Building Blocks’ self-learning algorithm gets smarter and smarter with each outcome. This in turn results in purer data as input to the CDP, which means you can be more accurate with your marketing communications.”
Erik adds, “The result of this golden interaction is a mass of organized and optimized data that you can deploy at scale to provide customers with the hyper-personalized experience they expect from companies.”
This appears to herald the end of gut feeling and interpretation.
Would you like to know more about decisioning engines?
Then download our white paper: “Waarom jouw CDP een AI Decisioning Engine nodig heeft.”