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Big Analytics Equals Fewer Gut Decisions


In the big data age, it takes continuous learning to find insights and multidimensional customer profiles to stay relevant.

In the big data age, it takes continuous learning to find insights and multidimensional customer profiles to stay relevant.

With so much big data in the world — digital, mobile, and social — organizations often struggle to develop clear, complete, and relevant customer profiles.

For instance, 39% of marketers say their customer data is out of date, according to a recent Columbia Business School study. Another study from the Aberdeen Group unsurprisingly found that top companies are more likely to rely on big data to guide their decision than their gut-based peers. (See also: The Value of Signal and Cost of Noise)

While most companies employ enterprise systems to develop customer profiles, and some even purchase external data to broaden their view, this traditional approach to customer profiles are incomplete in the SMAC Stack™ (social + mobile + analytics + cloud) age. Here's why:

1. Enterprise data is often dated, questionable, and restricted to past transactions, compared with dynamic, interactional data from systems of engagement that can be utilized to anticipate, even predict future customer behavior, wants and needs.

2. Purchased data is often extrapolated from customer surveys, which are often at odds with actual demand.

3. Customers look beyond official company information now before making a decision and are often less loyal than before.

In other words, creating customer profiles without holistically applying semi, unstructured, or traditional data is a great way to become irrelevant. Here's how to avoid that fate.

Follow Your Customer's Code Halo

To stay current with your customer, you'll need to track their social media activity, browsing behavior, mobile app downloads, games played, past purchases, photos shared, entertainment preferences, and vacation choices. We refer to this data as someone's "Code Halo™," which is essentially the digital footprint a customer leaves online.

Amazon, Google, and Facebook have quickly risen to the top by monitoring Code Halos, intending them, and building strategies around those insights. (See also: Code Rules A Playbook for Managing at the Crossroads.) Doing so enables them and other companies that have learned how to distill meaning from Code Halos to form a true view of the customer, rather than an outdated, generic or one-dimensional one.

Before you can update stagnant enterprise data to form multidimensional customer profiles, or Code Halos, customer activity needs to be properly analyzed and indexed. We call this process AIM (or analyze, index and meld) & Deliver. Here's how to do that.

Four Steps To Multidimensional Customer Profiles

Merge internal and external customer data.

With advanced analytics, this includes analyzing and indexing the customer name, organization, product name, and location parameters, then melding those with people, places, company names, and other entities for near-perfect attribute matches in the CRM system. After that, you can deliver the augmented customer profile enhanced with location intelligence to other systems for easy consumption.

Evaluate the business case with stakeholders.

This crucial step can make or break Code Halo creation. Key questions to answer include: How do you approach your first big data implementation? Do you have the information necessary to determine the approach? How can you ensure value of the big data journey? And what metrics and cost factors affect the success of your program?

Design the architecture and configure the analytics engine.

Once the business case has been crystallized, the big data architecture and analytics engine needs to be designed. The use case-driven approach can help map your requirements tightly with technology considerations, such as relational storage and query, distributed storage and processing, and low latency/in-memory. Successfully configured, this approach can produce qualitative new insights that result in reduced customer churn, next best action, and better risk predictions.

Enjoy real-time, multidimensional profiles.

Once the multidimensional customer profile is established, the possibilities are promising. In essence, the profile captures every digital trace the customer leaves, enabling your organization to not only make sense of big data, but derive meaning and act upon it.

Figure 1

Common Pitfalls, Looking Ahead

Once implemented, companies can expect several challenges when developing multidimensional customer profiles. These include data explosion (i.e., unprecedented velocity, volume and variety), reduced processing times on data, and customer privacy and regulatory issues. There's also the issue of letting customers decide what they do and don't share in their profile or halos.

But when consumers feel they're getting a tangible benefit in exchange for personal information, their resistance to data collection fades. Loyalty and rewards programs are good examples of how to persuade customers to reveal more detail about their behavior in exchange for perks. (Learn more about the "give-to-get ratio" in Code Halos ... How the Digital Lives of People, Things and Organizations are Changing the Rules of Business.)

Code Halo Solutions: Digital Privacy and Ethics

Today, leading organizations are already creating multidimensional customer profiles using both structured and unstructured data sources. The result is complete and continuously up-to-date customer insights enabled by big data and analytics. What company wouldn't want that in its arsenal?

For more information, read the full white paper, Turning Customer Knowledge into Business Growth (PDF), or learn more about Big Data Analytics.

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Big Analytics Equals Fewer Gut Decisions