Surprise! — big data analytics is not a Magic 8 ball that you can just shake for the answers.
Your customers know that they have handed you a lot of their personal information through a variety of interactions and channels. And your customers fully expect that your company has done its job and processed all that information to know what they want, how to give them excellent customer service and streamline doing business with you.
Customers don’t really care that you need to work through heaps of data to continuously update your customer intelligence. Customers only want you to shake the Magic 8 ball and have the right answer pop up. If the wrong answer materializes, customers know you aren't taking advantage of their information. Customers quickly conclude that you haven’t bothered to get to know them, meaning you really don’t care about them.
But you should care — and how you interact with your customers should show it.
From Big Data Analytics to Relevant Customer Intelligence
It would take a lot of different “big data” Magic 8 balls — testing, experimentation, creating different analytical and data models — to reach insight from the analytics.
Big data comes in many shapes and sizes; all kinds of big data sources can have value for marketing and sales. Besides social media and e-commerce data, sources such as machine-generated data (sensors, GPS and location, automated operational and digital networks) point to interesting possibilities. But it takes time and resources to explore and amplify those possibilities.
Certainly many companies have the resources to set up dedicated marketing science functions to include big data analytics in decision-making and innovation processes. But for many companies it may be just as likely that marketing can’t do this alone, due to budget and staffing limitations. Many companies may be better off centralizing all of their advanced analytics functions.
Centralizing analytics requires a strategic approach where data scientists and analysts collaborate extensively with the experts in different business functions — this is what is needed anyway to improve the accuracy and usefulness of analytics. Centralizing advanced analytics efforts will also spread intelligence benefits across the enterprise.
Centralized Customer-Focused Analytics
The starting point for customer-focused big data analytics shouldn't be Marketing strategy, programs or campaigns. It has to start as an initiative for the entire enterprise to learn more about current and potential customers and what they want and need.
First the organization should find out if it is even creating products and services that customers want, if it understands how customers want to interact with the company and make purchases. Key activities like customer segmentation isn't just for marketing — it is important for corporate customer strategies, innovation, product groups and sales.
Centralizing analytics can eliminate redundant effort to glean intelligence from big data and other sources. Adjunct analytics functions can be set up for individual departments like Marketing that fine-tune specific analytics and carry out special objectives that support departmental programs. Marketing and sales find the best success when all functions across the enterprise work in harmony. Centralized customer-focused analytics also contribute greatly to engendering consistent customer experiences across channels by sharing the right insight with all customer-related functions.
Another reason to centralize big data analytics is that big data alone is not enough. Big data is often fragmented and difficult to connect to specific customers or customer segments. Context, corroboration and relevance have to be introduced to link big data analytics to what the enterprise needs for customer strategies.
So many other data sources are needed, including master data which can provide what is missing from big data sources by making the connection to mission critical data across the enterprise. Master data management also connects analytics to the business processes that should be consuming intelligence and integrated data views.
Often marketers (and other departments) rely too much on their own silos of data and don’t include a wide variety of data sources. Centralizing advanced analytics should dynamically work to eliminate data silos in the enterprise and bring different perspective into the formulation of analytical models and a wider breadth of benefit to the enterprise.
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