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.
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
Surprise! — big data analytics is not a Magic 8 ball that you can just shake for the answers. Indeed, 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 eCommerce 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.
Big Data Analytics Mistakes Don’t Just Belong to Marketing
A bit too many articles are popping up on “what marketers are doing wrong for big data analytics”. Big data and advanced analytics are still very new – you could point to every business function in the enterprise for taking wrong steps with various analytics. A lot of hard work has to happen before significant and reliable results emerge – particularly those that lead to expedient decisions and actions. But that doesn’t mean it isn’t worth doing. Marketing can gain a great deal from advanced analytics.
Focusing on the customer and the customer’s perspective will help steer marketing and other business functions along fruitful paths. Outside-in perspectives keep activities connected to what matters most and should keep functions from detouring into internal-facing concerns that offer no value to customers. Advanced analytics based on customer big data won’t deliver value if the insights are diverted into “business as usual”: building marketing messages that try to persuade customers to buy products, instead of working from the customer perspective for buying journey preferences. Marketing might successfully use analytics to identify the right customers for the company’s products — but fail by taking the wrong course of action when communicating with customers.
Most of the Time Your Gut Isn’t Enough But…
Marketers are still relying heavily on intuition and past experience, and still lag on including well-crafted integrated advanced analytics in their decision-making processes. In most cases your gut alone doesn’t make a very good Magic 8 ball:
Our latest survey of 358 marketing professionals confirms – and expands on – these findings. Only 23 percent claim to be highly effective at uncovering new insights to generate additional business value. Only 25 percent claim to be highly effective at identifying and capturing new markets. And only 32 percent claim to be highly effective at engaging with individual customers.
82 percent of Traditional Marketers still rely largely on hunches and experience
But big data analytics alone won’t get the job done either. Combining advanced analytics with marketing experience and creativity is where marketing can shine. Making use of advanced analytics is not meant to replace creative vision and innovative ideas that come from the human element. Just as big data alone isn’t enough to connect to context and relevance, the same goes for marketing endeavors. Marketing functions need advanced analytics to keep the customer in full focus as the most important piece for context and relevance. 
The “gut factor” for decisions still matters. ‘Gut+Data’ adds intelligence behind brainstorms and fresh ideas. This combination connects the gut to what’s going on in markets and how customers are behaving.
The ‘Pure Gut’ road is a riskier path, but sometimes trumps ‘Gut+Data’, if the idea is disruptive and, of course, succeeds. In many cases, creativity is made better with insight from analytics for validation and amplification of the creative spark, as well as indicators of how to apply creative thinking.
