Updated: Jan 20
eCommerce Use Cases for a Recommendation Engine
Cross-selling and upselling are traditional merchandising strategies that retailers use to turn shoppers into buyers. The same tactics apply to eCommerce stores as well. Marketers need to recommend products for customers, not just miss out on the cross-sell or upsell opportunities. This will help them avoid losing potential lifelong shoppers, who might otherwise leave due to their lack of knowledge about these items.
Retail stores recommend goods so people don't walk away without being offered something beneficial, so why wouldn't customers expect it from an eCommerce store. At least a representative at a store will over a similar model or offer accessories that go with it. Well,
shoppers that click on a personalized recommendation are over four times more likely to complete a buy from your brand, so lots of stores send product recommendations via SMS or email, especially if they are using Shopify. No, upsell or cross-sell strategy would work as well as one powered with a customer data platform (CDP).
A recommendation engine works by collecting data, storing it, analyzing it, and filtering it. Because of the sheer complexity of these tasks and the enormous quantities of data they handle, a CDP is a must-have for a recommendations engine.
Cross-selling with better customer segmentation
Suggesting an additional item to accompany one under consideration may be easier regarding what bundles well with your product. Traditionally marketers first start with a one-size-fits-all execution, with product recommendations based on either past purchase and browsing behavior or on known characteristics of items like what's in stock. With limited data retailers, can only leverage a few factors like 25% of people who bought product X also bought product Y last year. If then statements can be limiting and marketers are usually looking for more granularity than the big segments. For your recommendation engine to work, you can't just set it and forget it. Customers are expecting personalization, so you need offers that it a specific target audience.
With a CDP, marketers have access to more information about customers than ever before, especially if your solution can look at deeper customer segments and personas. From what competitors they visit (this may require 2nd party data) and how often people search for certain keywords, to know when an advertisement will be most effective. CDPs help marketers understand their customer base, better marketers can take advantage of all this insight with just clicks these days.
Upselling with real-time insights
CDPs are specifically designed to connect and unify, creating a single source of customer intelligence. For a recommendation engine to work well, it needs to aggregate massive amounts of data which an eCommerce store conveniently has. Recommendations get better with better data like any other information solution, and a CDP can provide that data in spades. For example, customer data on past purchases, the timing of past purchases, communication preferences, and loyalty status can help you hone in on what the customer may need next. If you want to suggest a superior model at the premium price point, your strategy calls for data-driven personalization.
While demographic information like timing and geography can be helpful, smart marketers know that it takes more information to tell the whole story about the customers. Take two working-age women, both went to the same grocer and used a discount code, both in their 30s, one orders milk, and the other ordered wine. Is there enough information to understand why they purchased at that grocer for that product? What kind of hints can you get from behavior data related to eCommerce? We can try some of these.
Browsing product reviews
Submitting product reviews
Engaging with product pages
Frequency of engaging with product pages
Items added to cart
How CDPs solve specific cross-sell and upsell challenges
Marketing teams that can't successfully convert on cross-sell and upsell opportunities often don't have the data all in one place ready to convert. When your customer data is stuck in the various business applications you use, your customer experiences are limited. Data siloes cause disparate data from touchpoints usually associated with marketing – such as a CRM system, an email campaign, customer surveys, a website, etc. All this data needs to be put together. So that when a cross-sell opportunity presents itself and requires a real-time response to what should be presented, then the next best action to take at the moment of the interaction is served up.
When customer data is combined, these sources tell a story about a customer's journey. They are a rich set of signals, and for a marketer to understand the detailed nuances of each journey, there must be a seamless integration of all data sources, including survey data, and integration must be in real-time.
Connecting data and meshing it together doesn't yield results just like that. There needs to be a way to sort and process the data. Identity resolution is made by setting up an identity graph from often conflicting data – different names, addresses, devices, etc. – is essential to make sure the right offer is going to the right customer. Decisions must be made about what data to keep, how to prioritize it when to make a match, how to assign hierarchies.
The final step to using your CDP as a recommendation engine is making sure your customer receives the right information in the right channel at the right time. The fancy term to describe this activity is real-time channel orchestration. There needs to be logic that can quickly determine which cohort a customer belongs for a given campaign. Whether your customer uses channels like SMS, email, or paid advertising, you'll be able to reach them with this essential CDP capability.
It's important to be in tune with your customers and meet their needs. A CDP that integrates data from every possible source applies advanced identity resolution capabilities, intelligently orchestrates real-time decisions at a scale that can help a brand offer the fair value exchange for the customer. eCommerce marketers need a CDP-fueled recommendation engine to upsell and cross-sell.