“Customer engagement is where the heart is”
– Neil Patel (1)
A study by Constellation Research reported that companies who improve engagement can increase cross-sell revenue by 22 percent, up-sell revenue by 38 percent and order size by 5 to 85 percent. (2)
Engagement is huge. For some companies, it’s their mission: Maximize Customer Engagement. Think about big companies like Facebook or Netflix. They strive for our engagement. It’s everything to them. Without engagement, there’s no revenue or profit. Without profits, there can be no company (sorry WeWork). Instagram was massive before Facebook bought them. Instagram was (and still is) all about engagement.
But it’s not just these behemoths that have the data required to effectively increase engagement. In fact, most companies collect data that can help maximize engagement.
If engagement is a big goal for you or your company, you may be asking yourself how your data can help. Here are a few ways data scientists use data to help companies increase customer engagement:
Personalization is not only convenient for your customers, it’s what they expect! You know how you feel when your recommendations are spot on? It’s like they know you. When this happens, it builds trust and trust supports continued engagement. Here are a couple of ways in which we do that for our customers:
- Personalized Product Recommenders
Product recommenders are algorithms that drive engagement because they “pay attention” to each click or action a customer takes. Product recommenders use this valuable information to provide 1-to-1 personalized product recommendations in real-time. This is valuable in several touchpoints that drive up engagement:
- Product pages – tailoring the browsing experience to the customer
- Emails – recommending specific content for that customer
- Marketing materials designed to speak to specific segments
There are many ways to achieve segmentation for marketing, from splitting an audience on gender and/or age, to building informative customer avatars or profiles.When our clients have sufficient customer data, we can create very specific segments, and we see engagement spike due to hyper-personalized marketing. For clients with less data or who are just starting out, we can employ a few fluid onboarding questions to capture customer preferences. These preferences immediately impact engagement with marketing materials. It’s important to remember that when a customer gives us valuable onboarding data, it’s our obligation to act on that. For example, no meal delivery service should suggest steak to a vegetarian.
Reduce Poor Engagement – Predict Positive and Negative Reviews or Churn
To increase engagement, we often look at large collections of data showing customer churn. Then we build a model to predict when a customer has a high probability of churning. A retention team can use this information to intervene in real-time and hopefully prevent customer loss. The same process can be applied to negative reviews to predict these before they occur. This provides opportunities to reach out with personalized messages with the goal of re-engaging dissatisfied customers.
These engagement techniques are each data-driven. If you’re collecting customer data (to create rich customer segments), product data (containing the attributes of your products) and interaction data (how your customers interact with your products), you probably have everything you need to use these effective data-driven methods.
Many companies, however, collect piles of engagement data and never use it effectively. For example, have you ever received an email showing you a smattering of the latest products that you weren’t interested in at all? Unfortunately, these non-personalized messages are still common.
Showing a customer new or popular items is a solid first step towards driving engagement, but it never works as well as true data-driven 1-to-1 personalization. We usually see a 30% uptick in customer engagement when moving from such non-personalized approaches to true 1-to-1 personalization.
If you’ve made the transition to personalized treatment of your customers, please hit reply; I’d love to hear how it’s going! And if you’re interested in learning how your specific company/organization’s data can be used to enjoy the benefits of 1-to-1 personalization, feel free to reach out to us at firstname.lastname@example.org. The first consultation is always on us!
Read more on this topic here:
Links to referenced articles
Cutting Costs – New Hardware for Making Recommendation to Billions of Customers
The biggest recommendation services in the world are switching to powerful GPUs for their huge calculations and seeing massive benefits. They’re able to save time and expense by replacing large CPU clusters using hundreds of nodes with a single chip. In doing this, costs drop by 90%. https://www.nextplatform.com/2019/12/19/ai-recommendation-systems-get-a-gpu-makeover/
Most Hackers Aren’t Criminals
Read this interesting article about ethical hackers who spend their days breaking into secure systems before their adversaries do. It’s not A.I. specifically, but quite interesting. https://www.nytimes.com/2019/11/07/opinion/hackers-hacking.html
And for a bit of levity, check out this excuse generator you can use if you’re hacked: https://whythefuckwasibreached.com/
Kaggle First Place Winner Cheated, $10,000 Prize Declared Irrecoverable
Kaggle is a data science competition site, where data scientists (or teams of them) compete for cash prizes, usually by providing the most accurate solution to a problem. Over the years this approach to data science has proven controversial more than once. In this latest debacle, read about how a team obtained private data, constructed a fake A.I. model, and got away with the money from a platform for adopting neglected pets. https://towardsdatascience.com/kaggle-1st-place-winner-cheated-10-000-prize-declared-irrecoverable-bb7e1b639365