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Pulling Real ROI from a Sea of Big Data Analytics

In this special guest feature, Jessica Hawthorne-Castro, CEO of Hawthorne Direct, provides some strategic best practices for marketers that want to extract ROI from big data in order to improve the customer experience and generate sales. Jessica Hawthorne-Castro is the CEO of Hawthorne, an award winning technology-based advertising agency specializing in analytics and accountable brand campaigns for over 30-years.  Hawthorne has a legacy of ad industry leadership by being a visionary in combining the art of right-brain creativity with the science of left-brain data analytics and neuroscience. Jessica’s role principally involves fostering long-standing client relationships with the company’s expansive base of Fortune 500 brands to develop highly strategic and measurable advertising campaigns, designed to ignite immediate consumer response.  From strategy, creative and production to media and analytics, Jessica is committed to premium quality and innovation throughout all agency disciplines.

Enterprise firms are embracing data analytics to improve both internal processes and the customer’s total experience. Global retailer Walmart is building the world’s biggest private cloud which will crunch more than 2.5 petabytes of information an hour, including massive amounts of transactions at both retail stores and through the website. For example, if an item at the store is underperforming, the data analysts can judge if the error is due to a pricing mistake, customer sentiments, seasonal buying patterns, or a host of other issues.  The key element is finding correlations between a problem or a positive improvement and the actual data.

Developing actionable insights from the data is every marketers dream. The hard part is to understand how to generate and store enough of the right data so intelligent insights are produced, and then marketing decisions can be adjusted on the fly. Here are some strategic best practices for marketers that want to extract ROI from big data in order to improve the customer experience and generate sales:

Introduce Statistical Modeling

For marketers that are developing TV campaigns, there are now myriad data points that allow them to construct statistical modeling of campaign performance. For example, there is data on airing size, demographics, Nielsen data, and information on the actual stations and the timeframes for airings. To build accurate modeling, you need clean data from all of the available channels.

Clean the Data

The “garbage in and garbage out” adage applies to many aspects of business, and big data analytics is no exception. In order to find correlations and insights, marketers must be certain the underlying data is clean and relevant. Organization of the data on the front end will pay dividends later. Make sure data is scrubbed before it’s entered in the analytics engine and data warehouse, and that all possible channels of information are included. Data should be in place to inform marketing decisions before they’re made, instead of only relying on post-campaign reviews.

Track Behavior and Actions

The mobile site, main site, landing page, and social media pages can all be used to attract and convert customers, but they’re also overlooked as rich data sources. Marketers and IT should ensure each of their digital channels uses tracking pixels to follow each visitor’s actions. The sum of this data can then be analyzed to develop a “playbook” for every consumer, who can then be grouped together and approached in unique ways. Tracking should also include device information, so marketers can understand the habits of their mobile versus web customers and how to adjust the campaign content accordingly.

Look Closely at Retail Responses

Finding correlations between actions and campaigns is at the heart of big data analytics. Marketers should focus on retail responses among the various channels in order to develop correlations and then make the necessary refinements. An example could be digital video spots for a certain product are growing the sales of a related product that isn’t mentioned in the video. Marketers could use the analytics to find the reasons for such behavior, and then adjust content and channel mix to drive sales of both products. This directly impacts ROI, as analysis goes beyond simple campaign metrics, and instead has many layers.

Marketers should take a measured approach to big data analytics in order to produce measurable returns. The work on the front end to setup the right data is essential, as is the addition of new data streams that accompany each new campaign.

 

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