Converting Big Data into Useful Insights
The consumer packaged goods (CPG) industry is awash in Big Data, and that may
be good news. Recent research from Deloitte Consulting aimed to provide companies
with real and practical opportunities for leveraging the growing variety of available
data and to drive insights for more effective decision making, ultimately leading to
top- and bottom-line growth. Their recommendations ranged from establishing a solid analytical foundation to build on to developing a procedure of converting data into insights.
Is dealing with Big Data really a serious challenge for the industry? How can CPG companies convert emerging forms of Big Data into useful analytics and insights to improve business results? What are the risks of not dealing effectively with Big Data?
The biggest risk with Big Data is that it's, well, BIG. People get enamored with the “bigness” of the available data, so they ask what you can do with all that data, and they become overwhelmed with the possibilities.
Instead, I suggest you start from your objective, not the data. Ask yourself:
- What do you want to accomplish (more sales, fewer stock-outs, etc.)?
- What actions could you take to achieve those ends?
- What insights would you need to inform those actions?
- And what data do you need to deliver those insights?
If you happen to need Big Data, then at least you can keep the analysis simple and focused on a specific, immediately-valuable use case. And if you can achieve the same goals without using Big Data, then you will have avoided much unnecessary time and expense. Big Data should come last, not first, in your thinking.
Ben Sprecher, Commerce Strategy Lead, Google
The abundance of data has left many CPG organizations data rich, but insight poor. The ability to effectively harness the volume of data available and transform it into meaningful and useful information is a challenge.
Business Intelligence technology and Data Discovery tools are enabling companies to leverage analytics easier and faster, but you also have to explore the foundational elements of a strong analytics environment. In order to leverage analytics, companies first need to learn how to connect the dots and bring the data to life through context.
Delivering analytics so that even non-financial decision makers can understand the facts is also critical. Converting data into information, then information into meaningful insights is the best weapon to influence positive outcomes and ensure organizational success.
Big Data incorporates all data. Companies often refer to it as Volume, Variety and Velocity. Relational Solutions adds Complexity to that definition because leveraging Big Data does involve complexity and that complexity involves more data, code, processes, mappings, etc. But in the purest sense, Big Data typically refers to unstructured data on the web.
But how do you leverage that unstructured data? How do you use social media chatter, comments, announcements, blogs, photos, video, location information, etc? It depends on what you are trying to do with it.
If you are a CPG company that mainly sells through stores, there are several things you have to do. First, you leverage social media by having a means to track it, monitor it and manage your social reputation. You need monitoring tools to help do that.
Second, you need tools that will help you generate more information via the web. Those tools allow you to export results where the data can then be put into a structured format and analyzed against sales and other information.
You need to have a sound infrastructure in place that allows you to align the data with internal in a way that will make sense, provide more value and help you gleen insights into buyer behavior. You also need to be able to generate meaningful reports that will help you understand things like the effect of social media on sales, who your champions are, what marketing programs are working, etc.
Janet Dorenkott, Co-Founder and COO, Relational Solutions
An interesting trend has emerged lately in conversations with clients and prospects. The conversations begin and end with data. We talk about questions such as:
- What are my critical business issues?
- What data are meaningful?
- Why is there so much data?
- How do I make sense of all the data in my company?
- What is the difference between big data and data?
- How do I use all this data to make a difference in my business?
All of these questions are key to unlocking potential value, but one must begin by understanding the critical business issues facing the organization. Doing so ensures companies focus on those data attributes that are pertinent in solving the issues and prevents getting mired in overwhelming magnitude of data. For instance, by statistically determining the 18 most relevant data attributes, Inmar is able to reduce customer value to a single alpha-numeric value that allows clients to differentiate engagement strategies at the individual shopper level to build value. The approach is similar to how we humans process sensory data. Research indicates that we process and act on less than 5 percent of the sensory data that we receive (vision, smell, hearing, taste and touch). Perhaps we can begin learning about how we look at our corporate data by looking to how we as individuals interact with data.
Failure to understand and utilize relevant data out of the massive amount of data available will result in a diminished competitive capability.
Jim Deffenbaugh, Vice President, Data Sciences, Inmar
Ron Garmon, Senior Vice President, Analytics, Inmar
The use of big data to target and segment has always been essential for marketers. And with the growth of online media it plays an increasingly important role. CPGs can leverage the precision of direct marketing methodologies through CRM (customer relationship management) platforms to deliver the right message to the right customer at the right time.
Many direct response database management companies are using behavioral data and transactional attributes to help CPGs target online display ads. CRM platforms can provide information at the individual and household level to identify direct response purchases in areas such as family interests (women's, men's, children, pets, etc), health and well-being products and cooking and food enthusiasts. A score or data flag is transmitted in real-time (usually less than a second) to an online ad display platform - the ad network can then display the appropriate banner or pop-up that will direct the consumer to the CPGs website product category. For example, if a consumer has joined the Disney Book Club, it is likely they would respond to an online banner for children’s cereal; if a consumer has a subscription to Women’s Health magazine, targeting an online display ad for a low calorie meal makes sense.
The use of big data analytics and insights leverage the precision of direct response targeting to help CPGs capitalize on the all-important moment of engagement.
Robin Newhook, President, Newhook Marketing
The "volume" of data in and of itself that the CPG industry is awash with certainly makes it overwhelming for the CPG players to collect, consolidate, comprehend and convert all of this Big Data into useful insights. Added to this, CPG firms have to deal with a "variety" of different forms of related data. Complicating the volume and variety of data is the “velocity” with which these data streams change on a continuous basis and the need for the CPG players to understand these changes to stay ahead of the curve. Looking from a purely data perspective, all of these phenomena make it a serious challenge for the CPG industry to tackle Big Data. However, the industry should NOT be looking at the Big Data trends from a “data perspective,” but from a “business outcome” perspective in order to simplify the problem.
Most firms in the industry are looking at Big Data as a data deluge problem and are throwing strategies and tactics at it to analyze all that data. The approach to adopt should not be about “How comprehensively should one analyze Big Data?” rather it should be about “How comprehensively should one analyze Big Data that is relevant for driving a specific business outcome?” In other words, in the frenzy to understand all the Big Data streams, firms will get into the “paralysis through analysis” syndrome by embarking on comprehensive data analysis exercises that do not incrementally impact their critical business outcomes.
Instead, leaders in the CPG industry should first ask themselves what the critical business outcomes that they would like to analyze, predict and measure are? Once, they are clear on the outcomes they would like have a predictive insight into, then they have to look at Big Data as a weapon that they could unleash to achieve these specific business outcomes.
For example, if a leader in a CPG firm is interested in leveraging “Price” as the lever to maximize volume growth without compromising profitability, then there are much faster Big Data techniques, frameworks, and solutions that can help this leader with actionable insights that would help him/her move the needle in the right direction on the Price Optimization business goal. The key in this case, would be in choosing those Big Data solutions that are capable of sifting through all the Big Data streams of data and quickly eliminating those data elements that are irrelevant for solving the “Price Optimization” problem. If a solution does that early on in its analysis path, then one can quickly take advantage of the opportunity presented by Big Data without being slowed down by its sheer volume, variety, and velocity.
There are a number of parallel forces that influence today’s consumers’ choices. No longer can firms rely on the past success of their brands to achieve future market dominance. Analyzing, understanding and predicting what these forces are and how these forces are influencing consumers’ buying choices is a MUST to grow market share, maintain profitability and stay competitive. Intelligent application of Big Data makes this possible.
Phani Nagarjuna, Founder & CEO, Nuevora