Filling a big data gap

A Case Study

Why is it still so difficult to make decisions about marketing investment when we now have so much data available to us? Not only do we have sales, retail audit and media data, we also have new sources such as search and social data. But there’s a gap. What we are missing is a dataset that takes the customer’s perspective and answers the question, “How are people experiencing my brand?”

As marketers it is easy for us to draw a pie chart of where we are investing our money. We know whether or not we are investing more or less in TV and digital. What is much more difficult is to draw a pie chart of how people are experiencing our brand. What percentage of brand encounters are people seeing others using our brand? What about the percentage of times people see our brand featured in retailer advertising? Are these environmental experiences accounting for more than our paid advertising? How can we take decisions about where to invest if we don’t know what experiences people are having with our brand and with what impact?

LG Electronics USA has been capturing how shoppers for TVs and home appliances experience brands in the category for more than five years, providing a robust dataset to answer a whole host of business questions.

What is Real-time Experience Tracking?

Real-time Experience Tracking (RET) is a market research tool specifically designed to uncover how people are experiencing brands. The Harvard Business Review cites this approach as “a new tool (that) radically improves marketing research.”

At its heart, people use their mobile phone to tell us the answer to four questions about the experience they are having in the moment:

  • What is the brand? (e.g. LG, Samsung, Sony)
  • What is the occasion? (e.g. TV, In Store, Digital)
  • How did it make you feel? (5 Very Positive to 1 Very Negative)
  • How likely did it make you to choose this brand next time (5 Much more likely to 1 Much less likely)

This can be captured via app but we normally use SMS. This real-time data is then embellished with data provided by the participant in near time, including comments and photos about their experiences. This means we receive a much higher volume of data than traditional survey studies, providing us with a big dataset to analyze. RET participants have generated millions of real-time experiences, around 80% including qualitative comments and many having photos of the experiences appended.

Most of these studies have participants completing their experience diary over a week. Before they start this, we ask questions to understand people’s behavior and attitudes toward brands in the category. This way we can see who is strongly considering an LG TV and who is considering buying a competitor. At the end of the experience-gathering week we ask some of the same questions again so we can see what has changed and then, through advanced analytics, we can uncover exactly which experiences are having an influence on key brand metrics.

This means that the dataset is much more strongly linked to brand outcomes than traditional survey data enabling us to predict brand outcomes tomorrow through experiences people have today. For one client MESH was able to predict churn 3 weeks before the call center received customer calls.

Not only does the approach gather a large quantity of data, the real-time capture of experiences means that we can react very quickly. MESH has created a Rapid Response Campaign Evaluation tool that can provide Go/No Go Benchmarks by Day 3 of campaign launch with a full plan on optimization by Day 10.

The variety of data from quantitative, which indicates the big themes, to qualitative comments and photos, which help us to explain the ‘why’ behind the ‘what’, makes it easier to build a full story from the dataset. By conducting different pieces of analyses, from statistical modelling to uncover impact on brand metrics, to semantic and semiotic analysis on the qualitative material, we can improve our levels of confidence in recommendations where a number of pieces of evidence point to the same conclusion.

In comparison with traditional surveys asking about a pre-structured list of touchpoints, it is much easier to get more truthful and meaningful data from participants who are providing us with quick real-time feedback of their own. In one study for the male grooming product, Axe, we saw that young men were not just using the product when going out with girls but also when seeing their mates and “when auntie came to tea”. This indicated to us that using Axe was a rite of passage towards adult life.

What surprises has real-time experience data revealed? 

The power of retailer advertising. In our UK dataset MESH had captured experiences for three award-winning TV advertising campaigns. Around a third of experiences for these in real-time were Very Positive. Yet when tracking around Christmas another brand had over 60% of its TV experiences rated as Very Positive. Our client immediately thought there was a problem with the data because this competitor was not on air. He was right. The competitor had no brand TV advertising. However, this brand was featured in price promotions on TV across multiple retail chains like Tesco and Sainsbury’s. Wasn’t this devaluing the brand? When we read comments we saw people saying that Sainsbury’s was doing this advertising as a loss leader to get people into their store for Christmas shopping. So the advertising was attributed to the retailer and not the brand which led us to believe that this would not damage the brand as long as it was for a short burst. Since then MESH has studied across categories and countries and found that retailer advertising has a significant impact on brand consideration. Yet who is measuring this important touchpoint? It doesn’t get picked up in Share of Voice.

The importance of peer observation. 

Gatorade in Mexico was trying to re-position its brand from being a sports drink to providing sports nutrition before, during and after exercising. The team felt that communicating via “experiential” touch-points in gyms, fitness centers and parks would help the re-launch, but they didn’t know how to measure its effectiveness. Through Real-time Experience Tracking conducted over the six weeks of launch, MESH was able to see that people coming into contact with these experiential touch-points were twice as likely to positively change brand perception as if they simply encountered more traditional advertising. In particular, we saw one segment responding very positively to seeing others using Gatorade in the gym. From analysis across touch-points and competitors, MESH could see how best to adjust Gatorade’s marketing mix. The team took this advice and six months later saw the benefits in their brand metrics. Subsequently MESH has analyzed the impact of Peer Observation and found that this significantly impacts on brand consideration across many categories. We all know about the importance of word of mouth, so it stands to reason that seeing people using a brand will be important, yet how many marketers monitor this important metric?

Environmental touch-points, like those in the On Premise (restaurants, pubs and bars), can build brands. 

When Pepsi in the UK asked us which channels to activate to build closeness to the brand, we uncovered two, using advanced analytics: TV and On Premise. Yet Pepsi was not advertising on TV. What we saw was KFC featuring Pepsi in the end shot. Wasn’t this On Premise? In looking at photos and comments we were able to understand that putting Pepsi with food and in the context of a treat with family and friends, gave the drink a role in people’s lives. Suddenly the On Premise changed from being a volume-driving channel to one with the power to build the Pepsi brand through its positive associations. Which marketers look to their trade partners to build their brands?

What challenges does LG Electronics face?

LG competes in a range of categories from mobile handsets to home appliances to home entertainment. In addition to facing challenges similar to brands in other categories, there are some specific areas it needs to consider.

Each of the categories LG competes in represents high value, premium purchase decisions for the consumer. These products have relatively high price points. People need the product to last many years until the next purchase.

As a result, shoppers enter a fairly complex path to purchase that involves many different resources and influences on a journey that can last weeks or even months. \

In addition to these shopper challenges LG competes in a very competitive market, with some heavy spending from some well-known competitors. With these challenges, understanding shopper experiences throughout the path to purchase is critical to our success.

For LG shoppers, the path to purchase is truly omni-channel as they bounce back and forth between retail stores and online experiences. While it’s a little easier to see what shoppers are doing online, knowing what shoppers are doing in the store, and how these experiences fit into the broader path to purchase is harder to track. Shoppers are increasingly influenced by product reviews and recommendations from others. What percentage of a shoppers’ experiences are earned and what impact are they having on the shoppers’ purchase relative to other experiences?

LG was looking for an understanding of the shopper journey that filled these data gaps and captured all of a shoppers’ experiences leading LG to partner with MESH in 2010. How has LG Electronics benefited from this new data source? First, the ability to immediately see how people are experiencing brands in the category (see Figure 1). Figure 1: Where do people experience electronics brands?

Figure 1: Where do people experience electronics brands? 

 

This shopper-centric view of the world helps to guide the business on where to be investing at a broad level. We can immediately understand how important touch-points, like In Store, are for shoppers.

Within key resources, like In Store, we’re able to drill down to see specific sub-occasions. For instance, we can see what a shopper is doing while they are in-store based on their trip mission.

Retail touch-points also extend beyond the store itself. Shoppers experience LG and its competitors through retail touch-points via online retailer websites, store circulars and through traditional media, such as TV, where the retailer features manufacturers’ brands. These retail TV experiences can be as powerful as manufacturer driven TV experiences (see Figure 2).

Figure 2: Quality of experience for brand and retailer TV ads

 

In Figure 2, the size of the bubble represents Experience Reach (the percentage of people having the experience in the week they took part in the study) and the positioning on the map indicates Experience Positivity and Experience Persuasiveness (the percentage of experiences rated Very or Fairly Positive and Much more likely and Slightly more likely to purchase). Not only is Retailer Ad C, featuring LG, reaching more shoppers, it is as engaging as the manufacturer ad and even more persuasive than the LG brand TV ad.

Through the partnership with MESH and the use of RET LG has been able to:

  • Improve brand presence in retailer advertising
  • Leverage MESH data with key customers and optimize the in-store experience for shoppers
  • Optimize investment in the digital channel, through understanding how and when this is used within the path to purchase

Increase efficiency of TV advertising through knowing in what contexts this achieves the greatest pick-up from shoppers One outcome of these initiatives is that LG Electronics was the first electronics company to be awarded the coveted POPAI award for in store marketing effectiveness. Where to next?

150,000 experiences 

LG now has a wealth of data, including over 150,000 experiences, from which to analyze trends and answer business questions. With this data at our disposal we’re able to combine it with other data sources to take our analyses to the next level.

On a simple level we can measure the efficiency of LG marketing programs in terms of the cost to generate a shopper experience. This can be used to see which touch-points and executions are most efficient. We’ve used this information to inform media planning and to benchmark our performance versus our competitors.

Now, for the first time, we are integrating our Real-time Experience Data into Market Mix Modelling. In our past MMMs we would struggle to measure the impact of activities that were constant over the course of the year, like a display installation. However MESH data uncovers the quantity of shoppers’ who had an experience with that display (Experience Reach), and the quality of that experience (Experience Positivity). See Figure 3

Figure 3: The difference between traditional MMM data and RET Reach and Positivity. 

 

With this new data input we are able to more accurately measure our programs and inform our decisions.

In conclusion 

The LG Shopper Tracker developed with MESH is the main engine for insight at LG. It captures all key brand metrics as well as shopper experiences. The real-time data helps LG to work more effectively with retail partners because we understand exactly how shoppers are picking up on LG versus competitors in Best Buy versus Walmart. This enables the business to have better conversations with retail partners and to develop more productive relationships.

During the explosion of digital activations, MESH data has helped us to identify what works and what doesn’t. We can see Best in Class examples from the Home Electronics and Home Appliance categories to help with the briefing of external agencies.

The wealth of experiences that we now have in our database provides a fantastic start point to answer many of our business questions to the point where I now find colleagues around the business asking ‘Should we MESH it?’ By integrating MESH data into our MMM, we are ‘meshing’ it with other data sources for better explanatory power.

We have taken a journey over the last 5 years with Real-time Experience Tracking data, from simply starting to understand which experiences shoppers were having with our brands – to be able to draw the pie chart and take broad decisions on investment – to understanding the impact our activities are having on brand metrics and sales so that we can better deploy our marketing dollars across channels and tactics to make a difference at the margins too, delivering incremental value.

Big Data is essential to help marketers in their investment decisions but without a big data set that helps us to know how people are experiencing our brands we are missing half the picture.