Two of the hottest topics today, from the classroom to the boardroom, are Big Data and Analytics. There is a recognition that firms that do a better job of collecting, assimilating, analyzing, and using data have a built-in advantage versus their competitors. Yet, if your firm doesn’t have a competency in analytics, it’s difficult to figure out how to create one.
To better understand how companies can become more adept at leveraging analytics to improve decision making, I turned to Gopi Koteeswaran, the CEO at LatentView Analytics, a firm that specializes in helping CMOs at many Fortune 500 companies bridge the gap between the old and new analytics disciplines. What follows is an in-depth look at how best-in-class firms are leveraging the power of analytics to create advantage.
Q: How do you define analytics and how is it different from big data, marketing research, and other related concepts?
Data analytics, simply put, is the use of data to understand your consumer or business better. The data that you need to sort through and analyze can be of different sizes.When there is a lot of it, we call it Big Data.
To handle Big Data, you need to build a technology infrastructure to store, clean and convert that data (which often is unstructured or not of uniform type) to a form where it can be analyzed…and all this needs to be done in real-time. The thing about Big Data is that most of it is unusable to answer the questions you are asking; you usually have to sift through it to find the small, valuable nuggets. While this seems simple, this often is difficult. We’ve found that most of our clients don’t have expertise in organizing, structuring, and sifting through Big Data, which is the value that firms like LatentView can provide.
Once the data is “in shape,” new analytics applications have completely transformed the way companies approach marketing. You mentioned market research so let me pick that up as an example. Traditionally, market research was done through tools like surveys and focus group discussions. These are highly resource intensive, expensive and therefore infrequent. A large company might do it once a year to understand their consumers’ behavior and preferences. A sample size of 2000 is considered very large. All these tools have an inherent bias. A marketer has to draw the boundaries of exploration. He or she has to decide what questions will be asked.
Digital data on the other hand (i.e., things like html footprints that consumers leave behind when they visit your website or social media data) have significant value over these traditional tools in multiple ways. To begin with, by analyzing digital data you are ‘listening in’ to natural, honest conversations that are not limited. It isn’t a forced conversation. Second, the sample size is enormous. If you’re looking at 2000 consumers in a traditional survey, you’re talking about over 200,000 with digital data. Finally, the analysis is less expensive than traditional research, fast and therefore can be conducted multiple times in a year to answer different questions or hypotheses.
Let me give you a real example. We worked with a mobile phone manufacturer who had just launched a new phone. Through traditional research they had identified that the camera was the number one reason why people bought their phone. They wanted us to analyze this in greater detail. By analyzing digital data, we found that the camera was not the primary reason people bought their phone. As a matter of fact, the camera came in at number three. The first two reasons were an app that was bundled in the phone and durability. Data also showed that the superior performance of the camera in low light settings was the most appreciated feature of the camera. This brings me to the third reason why digital data can be superior to traditional tools. It provides depth and context to consumer conversations.
Q: How are analytics being managed within firms? More specifically, who is managing it and is it a separate function or housed within an established department (e.g., marketing)?
There are multiple ways in which companies manage analytics. The three most common are:
1. A centralized analytics function which works with different departments to help them benefit from the use of insights derived from data. In these cases you might have a Chief Data Scientist who heads that division.
2. An analytics chief, often the Head of Insights, who reports into a business or function head…often the CMO since he or she is the biggest user of data analytics.
3. Less common, market research in some firms is a centralized function separate from marketing and that unit is increasingly being staffed by data analysts.
Q: What distinguishes a best-in-class analytics firm?
The analytics maturity of an organization depends on a number of factors, the most important of which is the culture of the organization. Those that are highly mature in terms of their analytics capabilities prefer to rely on data rather than gut feel during decision making. As Edwards Deming of the US Census Board famously said, “In God we trust; everyone else bring data.” These companies typically have a highly technical team that builds and supports the infrastructure needed to collect relevant data. Of note, it’s impossible to get meaningful insights from data without having the right data.
The majority of companies function at the second tier. They know and understand that data can give them a competitive advantage and a few departments use data analytics better than others. So there are silos of analytics excellence with the organization.
The third tier is comprised of companies who are experimenting with data and analytics. They are running pilot studies to understand if data analytics can help their business
Q: Related to the above, if I wanted to measure a firm’s analytics ability, how would I do that?
1. Are different people across different departments using the same single “source of truth”…that is, are they all using the same data to make decisions?
2. Would your business leaders come to the same conclusion or decision, irrespective of who was making it?
3. How integrated is analytics into the day-to-day functioning of the organization? Is it integrated into all business units?
4. New economy companies typically have high analytics maturity because they need to be analytics savvy just to survive. So another good indicator is the percentage of their income that comes from digital commerce? The higher the percentage, the more analytics savvy they usually are.
5. How comfortable are they in using unconventional sources of data. Companies typically use internal data from things like CRM databases to run analysis. This isn’t enough, because in order to get meaningful insights they need to combine these sources of data with external sources like sensor data or geo-location data.
Q: What are the primary challenges firms are having managing analytics?
The biggest challenges companies face is in how to get their current decision makers to use analytics. Some companies are enabling this by setting up centralized analytics teams to drive the use of analytics across the organization. Others are automating analytics— providing their teams with at least a basic level of data to enable decisions.
However, one of the questions they need to ask is how analytics minded are the decision makers. And how can the company enable the firm’s leaders to be more data driven? Even traditionally creative functions are becoming highly analytical.
Obviously an important step towards this is to have the right data available to the right team when they need it. Another is to balance teams with people who have the desired skills for analytics. This brings us to the next challenge. Companies are seeing a shortage of skills required for data analysis within the United States. Right now, many companies are looking for outsourced solutions, like LatentView, given the shortage in skilled labor.
Q: Any examples of a best-in-class analytics firm that you can think of?
We’re lucky to work with many of the companies that are revolutionizing the way data is enabling businesses. For one of our customers, a food and beverages company, we use social media data to help them identify trends in flavors and ingredients that are going to be popular in the near term. We do this by drawing out conversations which have the highest volume in a defined time period and then mapping them against historical trends lines of flavors we know have been big in the past. By doing this we’re able to identify the next big flavor before it becomes big, giving our client a huge competitive advantage.
For another customer, a hardware manufacturer, we built an Early Warning System that will flag a customer complaint that is going to become critical (critical being defined in relation to the likelihood of the customer lapsing) before the incident occurs. As a result, the company saved millions of dollars in customer service that they would otherwise have spent in trying to get these customers to stay with them.
We have built predictive models that help companies predict a future event like the sales of a newly launched album based on certain indicators. This then helps the CMO decide what the next course of action should be— perhaps increase advertising spends and promos.
Q: Lastly, what advice do you have for C-level leaders to better develop and maintain an analytics capability?
1. Encourage senior managers to ask the question: how did you arrive at that decision? What data did you use? What analysis did you perform?
2. Ensure organization-wide availability of data. Ensure it’s high quality data.
3. Invest in analytics professionals in every department.
4. Benchmark regularly with best in class analytics firms.
Join the Discussion: @KimWhitler
About Gopi Koteeswaran: He is the CEO of LatentView Analytics, one of the largest pure play data analytics firms today. Prior to taking over the reins of LatentView, Gopi was a user of data analytics in his role as CEO of Philips Direct Life. Gopi has a proven track record of taking companies to positions of strength in diverse markets.
article by : Kimberly A. Whitler