Numerous challenges exist as enterprises embark on this next phase – managing the flow of incoming data of all types – most of it being unstructured, data storage, aggregating and integrating data.
To meet growing consumer expectations in a digitally-driven world, companies are dealing with large amounts of data and aim to create personalized consumer experiences at different touchpoints to stay relevant. The various consumer touch-points of an enterprise generate a wide variety of digital data and are also potentially great interaction points to improve an enterprises engagement with customers.
Furthermore, as enterprises adopt digital technologies for their internal and external processes, a large amount of digital data is available about consumers, products, competitors and the marketplace within and external to an enterprise. Establishing the business-value that can be generated from this data and developing or deploying viable technologies in digitally-driven processes is the current focus of most enterprises.
Numerous challenges exist as enterprises embark on this next phase – managing the flow of incoming data of all types – most of it being unstructured, data storage, aggregating and integrating data. Harnessing the integrated data and deploying it in various decision-making points by utilizing appropriate Artificial Intelligence-based technologies across the organization is another key problem.
A key aspect underpinning all these efforts is the need to maintain the data fresh and relevant – which varies across different touchpoints. Apart from the scale of the data – figuring out which bits of data are relevant and will have a business impact – is something every enterprise has to solve for at the earliest.
As a first step towards this comprehensive data-driven effort to improve customer experiences, it is important to have a good understanding of the issues involved.
Harnessing and Activating Customer Data – The Issues
Customer data currently sits across many silos in an organization – the marketing systems, the website, the Point-of-Sale systems, the returns systems, service and maintenance systems and many more. Integrating this data and providing a unified framework to access this data in a standard manner across the enterprise is essential. The data exists in many formats – structured, unstructured and a consumer has multiple identifiers across the different systems. Getting a unified 360-degree view of a customer is a key first step.
Pulling first-party and second-party data together in a meaningful manner and analyzing it for detecting individual and group trends and patterns at scale is a major task in knowing more about your customers and prospects. Much of the data needs to be cleansed and curated before it starts becoming valuable. Furthermore, data goes stale – as customers change their digital identifiers and personas. Keeping track of all these changes and propagating their effects is extremely important. Furthermore, the data needs to be made available at different touch points with the customer and also power a variety of decisions along the consumer’s journey within the enterprise’s purview. Once this data is made available, how to utilize it intelligently is where the AI systems play a key role.
Enabling Decision-Making – The Role of “Intelligence”
Customer-centric decisions come in two primary kinds – generic decisions that apply to groups as a whole and individual decisions – customized interactions – with an individual. AI systems play a role in both of these.
Decisions such as what product features to build, what product options to provide, what price points to refer to, what kind of merchandise to store at different retail locations, what combinations of products sell together, what kind of promotions etc are all powered by the availability of data. Artificial intelligence, machine learning and optimization systems help analysts explore the variety of choices available for each decision and then make the appropriate decision. For example, well-known techniques such as market-basket analysis, Pareto analysis etc. power these decisions. Apart from understanding consumer behavior with your own product, it is important to understand how customers engage with competitors.
Decisions that engage a customer at each individual touch point are far more complex. Firstly, reaching out to prospects or engaging existing customers is a major task in any enterprise. Marketers engaging in omnichannel marketing monitor all channels and engage customers appropriately based on the channel. Tracking an individual across different identities and engaging appropriately is a complex task. Different types of messaging and media types are evaluated on-the-fly by analyzing consumer responses to different messages and taking appropriate action. Making product recommendations to a customer – both online and offline – is powered by AI-based recommender systems.
A key aspect to realize here is that the “intelligence” embedded in these decision-making systems address the following key items: a) which data is relevant for each decision, b) how to get the data and summarize it, c) how to utilize the data to specify a customer-specific action that is relevant, d) how to deal with noisy/incomplete data, e) how to make a choice that satisfies the customer and is also economically beneficial for the enterprise. These steps need to be executed – some apriori and some at the point of engagement.
Leveraging customer data in enterprises requires a holistic and comprehensive approach. From gathering appropriate data, harnessing it and building AI-driven systems that deliver real value requires major investments in understanding the overall business processes and the underlying technology. Revisiting the core competencies of the enterprise and rethinking the overall consumer journey is essential to exploit advance AI and digital technologies effectively.