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Considerations for driving modern data-driven customer experience
Considerations for driving modern data-driven customer experience

September 30, 2024

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Authored by Sumeet Tandure, Sr. Manager, Sales Engineering- India,  Snowflake

 

 

Customers are spoilt for choices today, but finding the right value match takes time and effort. They are inundated with options and offers and have time pressures. Sorting through the information and misinformation to make the right choice on time takes a lot of work. On the other side, customer-facing sales, marketing, and services teams need help to make decisions to provide that value match. They must approach the right persona with the right product and decide on how, when, & with what message to make an outreach. They must make every interaction count and deliver delightful experiences over the customer's lifetime.

 

Over the past several years, organizations have collected many data points about customers, markets, and products, which can inform the alignment & outreach to customers very well. Making data-driven decisions has become a table-stake now & front-line teams must base or validate their decisions with data.  Today, every business - whether it’s a D2C brand focusing on millions of food-ordering consumers, a fintech offering micro-lending to students, or a B2B logistics company ensuring fulfillment on time -  has many touch points with customers that provide opportunities to collect data about customer interactions, products, and the ecosystem. We have deterministic data, which customers provide directly during the purchase or otherwise, and probabilistic data based on behavioral events from interactions with various engagement channels like Mobile Apps, Websites, etc. 

 

There are several martech tools & platforms, such as Customer Data Platforms (CDPs), which can instrument the customer touchpoints to acquire the data & use it for taking actions. Typically, some of the steps involved in this data-driven decision-making are acquiring &  storing data, making customer identity resolution, performing look-like modeling, creating a cohort or segment of users, mapping out the customer journey, creating a 360-degree view of the customer, determining following best action, activating campaigns via various marketing channels, measuring campaign performance, attributing the outcome appropriately.

 

One of the critical issues with the current data-driven customer experience is that it largely depends on the organization’s data, which represents an incomplete picture. Today,  every business works in an ecosystem, e.g. the merchants selling online goods & services, the digital payment intermediaries, and the last-mile fulfillment providers all are part of the overall value chain. All of them capture several different data points about the same customer. By combining these data points, we can create a much more complete picture of the customer.

 

Secondly, the platforms only act on customer and prospect profile data captured through various channels. They do not have transactional records and other internal data sets to juxtapose and make decisions based on this data at the right time. 

 

Finally, businesses have come to use a number of these tools & systems, which are doing some part of this overall workflow well but taking away the completeness of data. These systems tend to become silos of their own. 

 

Having a single place for all the data & the decisions gives organizations the control to understand & influence customer journeys holistically and personalize much more effectively. This requires a clear data strategy and a robust data platform to create a single source of truth while meeting privacy, security, and governance requirements.  Hence CDPs that execute their functionality on a centralized data platform- such as data lake, warehouse or lake house - instead of creating another silo of data & decisions are becoming more popular. 

 

Data platforms that allow organizations to combine second-party and third-party data make identification much more accurate and, thereby, personalisation much better. Advanced techniques such as data cleanrooms, which expose only selective data while maintaining privacy compliance while letting two parties collaborate to find overlaps and enrich data, are helping to find more attributes of an audience and what they are likely to be interested in. For example, some data providers today offer consent-based data about consumer preferences, which can be very useful for doing lookalike modelling and segmentation.  

 

In the era of machine learning and​​ generative AI, we also see organizations working on automating customer experience workflows—e.g. automatically classifying customer emails or call transcripts, generating the right content for the right situation, and augmenting decisions with an AI engine instead of only relying on human intelligence. Marketing teams can also leverage the data platforms' built-in AI/ML capabilities to drive their own decisions.

 

Modern AI & Data platforms such as Snowflake provide the ability to combine an organization’s own customer & systems data, along with corresponding second and third-party data points, very quickly and bring integrated AI/GenAI capabilities to this data. Organizations can then integrate the best customer experience tools with these platforms to derive customer-focused insights, make decisions holistically by looking at all this data and activate those decisions.

 

These are indeed exciting times to create value and a much better customer experience with the data, AI and associated technologies available at our disposal. One thing to remember is that data-driven is not a replacement for human intuitions & conventional wisdom. They both have to come together for effective decision-making.  An essential aspect also is to be very conscious about meeting & respecting customer privacy in the quest to provide the best experience.

 


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