Over the last decade, digital innovation, advancements in cloud computing, and fast-evolving consumer expectations have accelerated the experience economy worldwide.
Many enterprises are revamping their business strategies to meet the omnichannel care, personalized communication and instant gratification needs of today’s consumers. It gets more challenging as most of these enterprises are digital-first and don't have many physical branches or face-to-face interactions with customers. Most customer interactions and lifecycle processes are on digital channels for enterprises like Fintechs, gaming companies, OTT platforms and media streaming service providers.
So, now with less face time, how do you keep your customers engaged and drive long term value?
Acquired customers, but are they active?
The digital-first enterprises have developed a perfect recipe for customer acquisition aided by the digital marketing skill sets and tools at their disposal. For example, most of them have perfected the art and science behind increasing their app downloads. The pandemic also aided in accelerating this customer acquisition spree of these enterprises. But now enterprises realize that customer acquisition is not the end game. Instead, they need the acquired customers to be actively engaged to sustain the growth.
According to the latest global survey by Apptentive on App Retention benchmark, on average, 66% of the consumers are retained in the first 30-days of installing the app; that means during the onboarding phase itself. But, many enterprises are struggling to achieve this benchmark.
Thus, the recipe for acquisition is not working for them in creating long-term customer value.
So what is the recipe for customer retention?
For digital service providers to be successful, they must continually improve and revamp their customer retention strategies and concentrate on strengthening customer relationships.
Let us look at some of those ingredients in the recipe for customer retention:
Understanding and predicting consumer needs and behavior
Each customer is unique. Enterprises need to derive customer intelligence at scale. And this is possible only with AI that can analyze authentic first-party data on customer’s actions like purchase history, usage patterns, explicit and implicit feedback, etc, to give deeper insights into his current needs and expected behavioral changes.
Ensuring personalized experiences all the time
Once you understand each customer’s needs, you need to react to them in the moment of need to deliver a personalized experience. And this needs to be ensured at all touchpoints and stages of a customer journey, from onboarding to engagement and customer care, so that the personalized experience stays relevant and consistent. An AI can take this to the next level as it can predict the needs so that you can stay ahead and meet the need even before the customer realizes it.
Gauging and showing empathy to the customer sentiments
It is essential to listen to customers. Analyzing their online reviews, social comments or interactions with customer care agents can reveal a lot about a customer's state of mind. Analyzing such unstructured data unearths useful insights as to how customers feel about products, services, or offers. Here again, AI can help you listen at scale and gauge customer sentiments quickly.
Predicting and preventing customer inactivity and churn
Prevention is better than cure. It applies to customer retention too. AI/ML techniques are now common to predict the likelihood of a customer becoming inactive or leaving the business. However, it becomes more important to know why customers are leaving so that specific preventive actions can be determined before it gets too late. Advanced AI can not only predict churn but can also recommend the next best action to mitigate it by identifying the possible cause.
AI is the secret sauce that makes the recipe perfect!
As we have seen across these ingredients, AI is the secret sauce that makes the whole recipe perfect. However, it takes a blend of data science know-how, curated domain data and long training cycles to make this AI a reality.
The article has been written by Pravin Vijay, Head of Marketing, Flytxt