"Do you want fries with that?" This is likely what you will be asked next after ordering a hamburger or sandwich. If not fries, perhaps another side item may be offered that will appeal to your taste buds. In its most basic form, this is an example of cross-selling. To cross-sell means to sell additional goods and services to a customer who is already buying something from you. As consumers, we are presented with cross-selling offers based on buying habits and behaviors every single day. Whether its fries at a restaurant, a bagel at your coffee shop, shoes while shopping for pants...the list is endless. Some cross-selling opportunities like the ones mentioned above seem like obvious notions, but there are also many not so obvious cross-selling connections that businesses can develop.
Businesses can employ cross-selling techniques that will not only increase sales from existing customers in the short term, but also will develop a stronger relationship with customers for the long term. Long term loyal customers are essential to future business growth. To devise an effective cross-selling strategy for your business, machine learning is the way to go. Machine learning models can offer valuable recommendations to your loyal customers and likely increase their existing spend. With machine learning, we can segment customers into different groupings for you based on their buying habits and target the people who are the most likely to spend more in the future. We then create recommendation models for these high value customers in order for you to cross-sell to them in the most effective ways possible.
Who Are Your Ideal Customers for Cross-Selling?
In the customer journey, it is important to target the right customers to be able to increase their existing spend. The idea is to maximize gains in every phase. An important phase in the customer journey is the "loyalty" phase. Loyal customers are prime candidates to target for cross-selling opportunities. Loyal customers not only keep coming back to your business to spend again and again, they may even go out of their way to promote your products and services to other people (which makes them even more valuable as advocates). Word of mouth and online personal reviews are some of the best ways to promote. Cross-selling to your loyal customers makes sense, because the risk of losing them is relatively low and the opportunity of gaining from them is much higher.
We use machine learning to develop modeling that addresses each phase of the customer journey.
Use Machine Learning to Cross-Sell Using Customer Recommendations
So, we want to cross-sell, what strategies should we apply? What should we be offering? Machine Learning can help answer these questions for us. We can build the algorithms that will help us offer product recommendations in two ways. One is to use a customer's historical data to look at their buying habits and cross-sell based on that. The second is to recommend items to customers based on what similar people have bought in the past.
Machine Learning allows you to leverage historical data in hand to make more informed recommendations to your customers. How do you think Netflix makes recommendations? They match you with similar customers and recommend movies based on your watching habits. It's easier said than done because there is a complex algorithm running in the background that helps them do this. This is how companies are effectively making more money through cross-selling to existing customers.
Leverage Machine Learning to Cross-Sell to Customers and Increase Revenue!
Cross-selling is one of the most important techniques available for companies to increase revenue from their existing customers. If you cross-sell effectively, the risk of losing your customer is considerably low and the returns could prove to be very high. Creating smart recommendation engines, however can be challenging. That's where we come in. Let the Data Science team at Dunn Solutions build you the models that will drive successful cross-selling strategies and higher margins for your business.