Grow Your Customer Relationships With Analytics

MIT SMR’s 2018 Data & Analytics Report finds a link between customer engagement and data analytics.

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Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
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Mobile devices. Wearables. Sensors. Internet of things. It is no longer hyperbole to say that organizations can collect data from everywhere.

Despite that, collecting data just to have more data isn’t the point. Are the increasingly vast haystacks of data just adding more hay? If you are looking for needles in a haystack, adding more hay doesn’t help. But are some organizations able to use analytics to find more needles too? Are their investments in data collection leading to positive outcomes in, for example, customer engagement?

Released this week, our 2018 report on data and analytics, “Using Analytics to Improve Customer Engagement,” examines exactly this question. Building on a survey of more than 1,800 managers as well as over a dozen interviews with executives at global companies, our research examines how some, but not all, companies are able to use data from a variety of sources to yield superior customer engagement.

Across industries, our research illustrates that better customer engagement is linked to superior use of data and analytics. Overall, we find a significant rise in the number of organizations that gain competitive advantage using analytics; this year, 59% of our respondents believe that analytics creates competitive advantage for their organization.

A key component of this rise to 59% is more effective use of analytics to improve customer engagement. How? With analytics, organizations can develop detailed intelligence about their customer needs. With analytics, organizations can process feedback from multiple sources at scale. With analytics, organizations can tailor offerings specific to customer needs. With analytics, organizations can improve customer satisfaction.

Not every organization is successful at these activities. Many organizations already use data from customers, vendors, and competitors. However, in our research, the most analytically mature organizations are four times more likely to glean data from all three sources compared to less analytically mature organizations. These mature organizations are also much more likely to use a variety of data types — such as mobile, social, and public data — to engage customers. As a result, these analytically mature organizations are twice as likely to report strong customer engagement as the least analytically mature organizations.

We find that achieving this level of customer engagement requires well-developed core analytics capabilities to ingest data, to analyze using sophisticated techniques, and to apply the resulting insights into routine processes. Many organizations have made the necessary investments in developing their core analytics capabilities and are now reaping what they have sown. Furthermore, companies that have businesses as their main customers (B2B) are harvesting the most benefits, in part because they share data with customers in ways that directly strengthen relationships with them.

Despite our new findings about how organizations can be successful with analytics in creating competitive advantage and engaging with customers, many of our “new” findings are old. Organizations face several enduring challenges in their use of analytics, including:

  • Difficulty managing the human and data partnership, particularly with gains in artificial intelligence creating flux in the partnership.
  • Dependence on leadership and culture to shift to a data-driven focus.
  • Uncomfortable organizational change as use of data becomes more and more pervasive, even in areas previously distant from data.
  • Imperfect metrics that, as abstractions, may be difficult to get right.
  • Persisting struggles with data quality that limit potential.

If success in the face of such challenges seems like a fairy tale, it’s not. The organizations that are successful with analytics haven’t found any magic beans that grow their analytical capability to unprecedented heights, nor is there a magical analytics golden goose. Their successes are the result of hard work that is allowing them to benefit from the many emerging sources of data to engage with their customers.

For more details about how organizations as diverse as WinField United, Mall of America, Redfin Corp., Bank of Montreal, Wescom Credit Union, Siemens AG, Devon Energy Corp., Oberweis Dairy Inc., Swiss Re AG, American Eagle Outfitters Inc., Nielsen Co., Lenovo Group Ltd., and many others are using analytics, please see our report “Using Analytics to Improve Customer Engagement.”

Join report coauthors Sam Ransbotham and David Kiron for a live Twitter chat to discuss key questions related to this year’s data and analytics research on Thursday, March 1, at 11 AM ET. To join, simply follow the hashtag #MITSMRChat on Twitter at that time.


Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
See All Articles in This Section

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Comment (1)
Dr Rabindranath Bhattacharya
Dr.Rabindranath Bhattacharya, VGSoM, IIT Kharagpur
I find Three Laws mentioned by somebody very interesting. I summed it up as follows
Law 1: “More data” should not harm on- going analyses
Law 2: “More data” should be added only if it refines the result to a great extent practically and its addition does not conflict with the First Law
Law 3: “More data” should be added only if its addition does not exacerbate existing biases in the data
In fact, too many data sometimes make the analysis very complex and time consuming and if you aim for very highly accurate results you are probably running a wild goose chase. My suggestion is that be practical and try to aggregate the data in such a way that final results are not affected to a great extent. For example if you are collecting 5-digit pin code data of customers for network analysis across the country it may run into millions. Is it possible to feed such a huge data into the computer and get the result? Certainly not. However if you aggregate the data as per last 3-digit pin code problem may be manageable without sacrificing much the accuracy and biasness. Design of experiments (DOE) also is one of the methods for working with lesser data and get the desired results. Data from vendors and customers are exploding so is the analytical ability of the Engineers and Reserchers in the field and I believe our future is in safe hands.