Using Big Data to Drive RevenuePositive Results From the Smart Use of Big Data Analytics (3rd in a Series)

I recently spoke with several high level marketing executives about the near-ubiquitous topic, BIG DATA. The executives included Paul Golden, ex-CMO of Samsung Mobile, Barry Judge (ex-CMO of Best Buy, current CMO of LivingSocial, and Brad Todd, (Principal at The Richards Group). In this third installment, we review some of the results these executives experienced.  Big data analytics was the key in making the information they had actionable to drive customer value.

Brad Todd has helped clients use their data in very sophisticated ways, by applying rigorous big data analytics.  A home improvement retailer, for instance, has used information from their customers’ do-it-yourself projects to engage in helpful conversations with their customers. This type of engagement not only makes the customer feel valued, but very often leads to follow-on projects and increased customer loyalty.  For instance, if a customer has planned a deck using online tools, the retailer can follow up with them at predicted intervals with suggestions and relevant offers to improve the likelihood of purchase.

The Richards Group also helps their clients integrate their marketing data and then apply big data analytics, with the objective of personalizing customer communications.  They have seen improvements of 20% on average when website, email and remarketing channels are personalized to customers.  The results are even greater—about 25% if cross-channel personalization occurs.

At Samsung Mobile, Paul Golden used longitudinal brand preference data to prioritize markets for their marketing efforts.  He and his team tailored brand messages and tactics for eight key markets to improve brand preference versus a key competitor.  The result was a swing from a relative score of -6 to +2 in overall brand preference, despite only focusing on eight key markets.  Big data analytics allowed Samsung Mobile to cost-effectively determine which markets would swing the entire country’s brand preference score in their favor.

While CMO of Best Buy, Barry Judge and his team applied big data analytics to vast amounts of customer information to zero in on their highest value customers.  They then tailored all their marketing to best serve those customers and increase their engagement.  Knowing their customers and what their shopping habits allowed Best Buy to offer the most relevant products and offers to promote via email and direct mail.  By focusing on their most loyal customers, they grew their loyalty even more and increased their share of wallet with these customers.

Big data can be a big deal in driving results for brands if used to improve customer interactions.  Set objectives, determine what data is needed to achieve those objectives, compile and analyze the data, then translate it into something valuable for your customers.

Want to learn more about how to use big data analytics to improve business results? Click here.

And please feel free to leave any comments or questions below.

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Creating an Omni-Channel Customer Profile can be Easy, if you Start with the End in Mind

omni channel customer profileWith all the clutter of marketing messages, customers are demanding relevance. At the same time, marketing teams are struggling with some of the basic foundational components because of all the disparate sources of data available both internally and externally { there I stayed away from saying big data } …The ability to communicate with your customers in an individual manner is becoming table stakes in both online and offline marketing, what we at Nectar Online Media like to call Hyper-Personalization. Whether you use the term 360-degree customer profile or omni-channel customer profile, the goal of creating a unified picture of your customer’s data is foundational for accurate customer analytics and also hyper-personalizing your interactions with your customers.

In this post, we thought we’d provide some of our tips for how to build an omni-channel customer profile. If you start with the end in mind (i.e., your marketing or business objective), it will be a lot easier.

 

# 1 Know Your Goal — It sounds simple and we’ve heard the same tip for many other areas, both in business and personal life. As it relates to customer analytics and hyper-personalization, the goal is based on how you want to use the customer data and, therefore, impacts the data sets you really need vs ideally want to have. By selecting the right data sets for building your omni-channel customer profile, your internal business partners and external providers can be much more focused (and efficient).

For example, Nectar works with an online ecommerce retailer, hipcycle.com, to help personalize their digital communications { if you’ve not checked out Hipcycle before, I strongly encourage you — you won’t be disappointed }.

Based on understanding Hipcycle’s marketing business objectives, we were able to hone in on the right data sets to integrate. These data sets were primarily based on transaction, crm, and behavior on hipcycle.com. While data sets like social media and household data provide an interesting lens, these data sets were not going to add incremental benefit & results that outweighed the effort.

 

# 2 Marketing & IT Need to Collaborate — While the marketing team can help define business objectives and outcomes based on using the omni-channel customer profile, the marketer’s technology counterparts are pivotal in articulating in identifying road blocks ahead of time and developing the right data streams.

If the marketing group is defining the customer analytics and hyper-personalization needs, involve the technology teams early on in the process to be better informed on constraints, timelines, and the ‘art of the possible.’

 

# 3 Choose the Right Technology — Different technologies are appropriate for different business objectives. If you are aiming to build an omni-channel customer profile, our experience has found a traditional SQL (row & records) environment is not optimal. Why? In a nutshell, because of all the different data sources and likely millions of records, there is a fair amount of processing a system needs to do before you can see the results (analysis, reports, recommendations, etc.) that you are looking.

At Nectar Online, we’ve found a noSQL environment is much better suited for storing data records for the purpose of utilizing that 360-degree view of the customer. The primary benefit is that data is stored in an array … so at the instance when data needs to be processed for an individual customer, information is ready.

 

# 4 Relevant Refreshes — An important component to evaluate is the frequency of your omni-channel customer profile refreshes. Depending on your goal { see how knowing your objective comes back in }, a different refresh or re-scoring frequency may be needed potentially at a data set level.

For example, if you are using social data to identify key life events of your individual customers, a weekly refresh might be sufficient. However, if your goal is to create a trigger event based on an abandoned cart, having this behavior refreshed in real-time is important.

 

# 5 Test & Learn — In the same way that a customer’s behaviors, habits, and interactions change over time, so do requirements on how you are using the customer profile data. By having a specific testing and learning plan identified prior to embarking on building your initial omni-channel views, the marketing and technology teams can better determine what elements are important for consideration.

In addition, as the customer profiles continue to be refreshed, you will be able to identify additional revenue and engagement driving opportunities. The testing and learning plan establishes the right set of performance indicators for what you are looking to accomplish.

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I’d love to hear from you and learn about your experience building omni-channel customer profiles. What other tips have you seen be helpful?

Drop us a note or share a comment below.