The concept of recommendations has been around since Amazon first added “inspired by your shopping trends” or “top picks for you” to its website.
When customers are on the lookout for an item, they are hoping to find the best possible one out there. Their search is comprised of hundreds, if not thousands, of digital touchpoints. Through these interactions, customers expect brands to learn about them. In fact, they’re willing to spend more money if they receive a thoughtful, targeted experience.
While generating recommendations has become fairly standard, there continues to be an increasing level of frustration when they’re not personalized.
One of the key reasons is that growth teams have approached recommendations as a short-term product-centric strategy instead of a long-term brand strategy. So recommendations are seen as a means to drive more conversions on the website or app instead of using them as a sustainable way to increase customer lifetime value and loyalty and eventually optimize the end-to-end customer experience.
Use Recommendations for Long-Term Growth
So how do we bridge this gap from a short-term campaign strategy to a long-term growth strategy? How do we ensure that we account for rapidly evolving lifecycle stages?
Use data science to map evolving consumption patterns
Customers make purchase decisions on a number of factors that are constantly evolving. If growth teams can find a way to stay on top of these changes, they can provide the best possible experience while seeing a tangible boost in revenue.
A report on personalization from Segment assessed how it impacts shoppers1. Findings show that 40% of consumers have purchased something more expensive than they had originally set out for. Moreover, nearly half (49%) of the shoppers made an impulse purchase after they received a personalized recommendation and a majority (85%) of them were very happy with their decision.
It would be impossible to keep recommendations relevant to each customer in real-time without using a scalable data science model that works well for your business. Data science helps automate customer recommendations to not only make them relevant to each user but also to consider the dynamic changes that occur along the way.
For example, if you recommend Colombia Phone Numbers List vacuum cleaners to someone who just bought one, that message is bound to be ignored.
Similarly, if you recommend chocolates to a former chocoholic who’s now buying more fresh vegetables from your grocery app, you aren’t keeping up with her updated demands.
Integrate your engagement strategy with a recommendation strategy
Unless your engagement tactics are connected to the recommendations tactics, customer experience will continue to be disconnected.
A recent report2 on recommendations for e-commerce shows that the average number of items purchased increases by 50% when customers actually engaged with the recommended items – through clicks, impressions or purchases. It further finds that the average order value of a recommendation that catches a customer’s attention increases by nearly 33%.
This means that once you have created a set of data science-enabled recommendations, you want to find a way to engage with those recommendations at the best possible time on any number of channels.
Enhance Engagement with Product Recommendations
Using CleverTap, you can easily identify and automate ‘who to send’ and ‘when to send’ your campaigns.
But now with Product Recommendations, we’ve taken the next step to assist you with your engagement strategy. With this latest feature, you can automate the ‘what’ of customer interactions with 1:1 personalized recommendations that dynamically adapt to customer purchase behavior, buying patterns, and usage trends.
Using an AI-powered system that allows complete control over merchandising, growth teams can create intuitive targeting rules for millions of catalog items. This means our recommendation engine can generate recommendations unique to each user.
Once the recommendation engine generates specific content for each user, growth teams can send these out via In-App, Push, Webhooks, SMS, and App Inbox. They can build rich marketing communications using images, videos, deep or external links, with custom fields in their catalog definition to personalize the message. Omnichannel campaigns can also be triggered based on specific user behavior such as “add to cart” or “searched” to bring further context.
Use Data Science to Avoid Recommendations Becoming Irrelevant
You can create multiple types of recommendations based on different filter criteria.
Make real-time recommendations based on user actions
e.g. Trigger In-App recommendation when a user adds to cart.
Customize recommendations based on customer segments to upsell and optimize inventory
e.g. Generate recommendations with only high-value items for champion users.
Generate recommendations with only high-value items for champion users.