Six Tips for Extracting Big Data Insights

Amazon is a market leader at using Big Data, both in terms of the refined suggestions it offers to consumers, and internally as the company processes millions of transactions and shipments. It reviews all of this information to streamline the customer experience and to refine its own processes. Another example can be found with American Express, which uses predictive analytics to identify the most loyal customers and offer them retention incentives.

These companies have something in common: the ability to manage massive sets of data and pull actionable insights. This is the tricky part for marketers who are striving to find ways to dive into data and generate the right insights that can then affect marketing decisions.

Consider these six strategic tips for creating positive ROI through big data with improved campaigns and targeted messaging:

1. Use clean data. Computers operate under logical constraints. Within the big data context, this means the outputs and resulting insights can only be as good as the underlying data. If the data entered into the analytics engine is not well organized or is non-essential, then even the best data scientist won’t be able to extract value. Marketers should work with data that has been thoroughly scrubbed before it goes into the data warehouse. Businesses should generate and store as much data as possible as it pertains to every aspect of the business that can affect a campaign. The key is to proactively build the data, not to think it’s easy to add the data after campaign launch.
2. Introduce pixel tracking analytics. Marketers should leverage their company’s website, not only as marketing tools and a conduit for sales, but as a data generating vehicle. IT and marketing can work together to introduce pixel tracking, where tracking is placed on the various websites used by the company, whether it’s mobile, a microsite, or another location. Social media information can be tracked and analyzed using social media pixel tracking, so you can understand how Facebook ads are performing. Such tracking also provides user device data, so marketers can better understand if sales are coming from mobile or web consumers, and can see the trend over time.
The point of this collection is to have enough rich data where you can build behavioral categories for consumers. By creating “personas” for different segments, you can then find correlations between certain behaviors and buying decisions. Then, content and various options can be presented to these customers in a targeted way that enhances their experience and provides relevant content. Firms are using browser search history tracking to adjust what offers they might receive.

3. Use statistical modeling. Marketers developing TV campaigns should take advantage of the improvements in data collection that allow them to adjust the campaign to match actual results. Metrics on the stations, airing size, demographic information, second-screen activity, and Nielsen weighting points can all be combined to create statistical models. Collect clean and granular data from all channels to inform your analysis. Much of this work will need to be done after a campaign, using regression analysis, cluster analysis and logistic regression, among other techniques.
4. Target specific demographics. Big Data is essential for targeting because it provides markets with precise direction on where consumers live, the devices they use, their search habits, and other behavioral metrics. Armed with this information, marketers can leverage more ROI from their digital media and TV placements. Big data effectively makes the marketers and campaigns more intelligent and allows for granular messaging that might differ markedly from one channel to the next.
5. Use mixed-media modeling. Analyzing sales and response data is the core part of mixed-media modeling. It helps marketers to properly weigh each channel to weed out the under performers and direct more budget to the channels that are outperforming. Identifying the best channels will become more refined over time, as the data itself will improve as well as marketer’s ability to proactively analyze the data and correlate information and future results. With today’s highly fragmented digital media environment, performing modeling can be priceless as it gives marketers more focus on picking the right channels that will deliver real measurable results.
6. Gauge the retail responses. Big data analytics is largely about finding correlations. Action A results in an increase in Action B. Marketers can measure retail responses across channels to discover such correlations. Perhaps TV spots for a certain product are pushing overall brand response in terms of increases in mobile site visits. Big data can uncover the purchasing habits of various customer segments, helping marketers to develop cost-per-acquisition and other related metrics that directly show ROI. Understanding which consumers are performing which actions through big data analysis can directly impact sales and drive consumer demand.

Leveraging big data analytics to improve ROI is not as complicated as it might appear at first glance. Marketers who want to improve multi-channel sales and the customer experience should take a deliberate approach to big data analysis. Every campaign should be crafted based on data on the front end, and its performance should be evaluated on the back end in order to refine future campaigns for maximum ROI.


Source: InformationWeek

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