How Next Best Action can deliver higher customer value
The recommendation engine or Next Best Action plays an essential part in influencing human preferences. Whether Amazon, Netflix, YouTube, or any other major company, we use their recommendations in our day to day lives. The next best action or the recommendation engine has a significant impact on the business performance both in terms of revenue generation and customer satisfaction.
So why do operators need Recommendation Engine? Most of the telecom operators have a customer base in millions and hundreds of products. With so many products to match with customer’s, how do you identify the best product fit for each customer? And not to forget the multiple variables that are defining the customer behavior and needs.
Businesses can use the output from the recommendation engine in multiple ways to drive their strategy. If the plan is to upsell or revenue enhancement, the business user can put an extra condition in the model that the price should be higher than the current subscribed price. If retention is the objective, a business user can set conditions for the recommended product to have the same price or less than the existing product customer is using. In the cross-sell case, we can follow the same strategy as the business wants customer exposure to new products.
Other than tangible benefits that a recommendation engine can offer to business; it can provide a lot of intangible benefits to customers, like better experience, simplicity to choose, and satisfaction, leading to higher customer loyalty.
Different approaches for Next Best Action
The most popular techniques used are collaborative filtering and content-based filtering. Collaborative filtering means filtering out products that a user might expect based on actions by similar users and content-based filtering, also known as cognitive filtering, where product recommendations are made based on a comparison between the content of the items and a user profile. The collaborative filtering approach assumes that consumers with a similar profile may have the same needs and make similar choices while in content-based, the historical customer data form the basis for the recommendation.
There are many other non-traditional and much more advanced methods such as deep learning, social learning, and tensor factorization that are based on machine learning and neural networks to power the recommendation process and take it to the next level. It will create a much efficient process for CSAT and retention.
Building the recommendation engine is not enough
Operators need a clear objective when implementing the next best action strategy. The recommendation engine might recommend a lower price product as the best fit for customers leading to revenue loss for the operator. So, it’s necessary to identify that the best-fit product meets both customer and operator objectives.
Also, telecom products have different validity, which means a customer who had bought a product with 60 days validity will buy only after 60 days. So, the model must account for this purchase gap while recommending new products.
One challenge that we faced with one of our clients was that customers were buying multiple similar products in a given month, making recommendations difficult. To overcome this, we decided that under each product type – Data, Voice, SMS & VAS, we would recommend the top 3-4 products. We accomplished this by providing rankings for products in recommendation basket. Ranking also offers flexibility to the business user that they can further add more conditions on products based on their strategy, such as up-sell, aggressive -sell, or cross-sell.
We are currently achieving 70%-80% accuracy in the top 5 recommended products for different customer value segments.
Do you need the Next Best Action strategy?
Using customer interactions or insights is the best way to identify your customer behavior patterns and use them to provide the best recommendations to your customers. A recommendation engine can help you identify customer behaviors, buying patterns, and translate the insights into a hyper-personalized approach for your marketing campaigns. The result is a more in-depth user segmentation with clarity, higher LTV, and higher customer retention.
Here is a Point of View on the importance of the Next Best Offer or Recommendation Engine for your business.
Harish currently serves as the Director, Analytics & Insights portfolio at Subex. He has 12+ year of experience in Marketing Operations, Customer Value Management, Optimizing Marketing performance, improving Customer Experience via data analytics, technology and platform.