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by Stacy Gordon, CMO and Business Head – US East at LatentView
Technology and the Rate of Change
Technology is accelerating the rate of change and these changes are truly reshaping all of our lives. Remember when newspapers would break a story overnight? Today, Twitter is breaking news – from the deaths of Amy Winehouse and Osama Bin Laden to the US Airways flight landing in the Hudson River to the discovery of ice on the moon – and in real time. Or, remember when taking photos meant finishing up a roll of film, waiting for the pictures to be developed, then sharing prints with a few close friends and family? Today, the time between snapping a pic and sharing it with the entire world has shrunk to mere seconds.
In this world of hypercompetitive markets and fickle consumers, businesses have to innovate to thrive.
But Where’s the Innovation in the Product Innovation Process?
While the world is increasingly speeding up, you know what hasn’t? The traditional innovation process. Ironic, no?
Here’s what the typical process looks like:
You start with ideation – usually internal or sometimes with focus groups – and go through some kind of sorting or prioritization exercise. Then, you do some concept development or surveys before moving to field and market testing. Lastly you move to commericialization where you launch and assess the results.
Your organization’s specific approach may have fewer or more stages, but this process currently still takes 18 to 24 months on average. And we all know the challenges: it takes too long, costs too much, and in many cases, you end up with products that in the end aren’t all that innovative. I’m not bashing traditional methods; they’re great and have stood us in good stead for a long time.
But the time is ripe for disruption.
New analytical methodologies can transform innovation at its core, allowing companies to test new ideas at speeds (and prices) that were unimaginable even a decade ago. For example, when you look at the ideation stage, what if instead of sitting around a conference table – or even bringing together a focus group of a handful of consumers – you are able to capture what large numbers of customers are actually saying about your products and services? Then when you move to selection, you’d be able to prioritize based on their needs and field testing would capture real-time feedback and enable you to truly support a fail-fast approach. And post-launch, you could continue to track consumer response and identify line extensions.
A New Approach Requires New Data Sources
There still is tremendous benefit in existing inputs, but there is a wealth of data out there today that can provide valuable information at speed and at scale.
In fact, there’s so much of this data that it can seem impossible to harness it. That’s where analytics comes in. Social and unstructured analytics enables you to understand consumer trends within the market, assess how consumers react to product concepts and launches, and understand the emotional and technical needs of consumers within a specific need space. Bringing this level of insight to the innovation process helps:
• Reduce time to market during the ideation and concept testing stages
• Reduce cost of research by replacing expensive data sources and process steps
• Improve relevance by putting the consumer at the center of the process
Case in Point: Late for (All-day) Breakfast?
In October 2015 McDonald’s launched all-day breakfast. Sales went up and competitors reportedly took a hit. But, in fact, social analytics shows that consumer interest in all-day breakfast was evident a full two years earlier. The chart above shows the volume of social conversations around a McD all-day breakfast – and you can see a significant inflection point in October 2013. With this kind of data, it’s likely that McDonald’s could have enjoyed the increased sales and competitive edge much sooner.
Case in Point: Lower Costs and Increased Agility
Of course, it’s easy to be a Monday-morning quarterback, so here’s another example of a major consumer technology company that used social and unstructured analytics to drive significant business value. Previously, the company had been using multiple, more traditional data sources to analyze customer reaction to new products, which was expensive and difficult to get a complete picture. They analyzed 100+ million conversations across 10+ brands. The insights generated from this near-real-time data kept senior management updated with critical reports that enabled rapid campaign response and crisis detection. Some of the different analyses conducted were:
• Topic definition – tagging the social media content to a topic
• Sentiment analysis – tagging the social media content to positive, neutral, and negative sentiment
• Exhaustive theme generation – identification of trends and themes that commonly appear together
• Influence analysis – identification of key promoters and detractors for brands and sub-brands
• Deep-dive analysis – identification of granular and actionable insights using social data
• Visualization – ability to answer business questions with world-class visualization using infographics, PowerPoint, and Power BI
Social analytics replaced slower and costlier traditional approaches, saving the company $5 million annually and increasing agility with real-time feedback on product launches, competitive comparisons, and crisis management.
A Few Parting Questions
If you’re looking to transform and accelerate innovation within your organization, here are some questions to ask yourself.
• Are you using any new sources of data?
• Are you thinking about ways to leverage social conversations beyond tracking likes?
• Are you letting your innovation be driven by your consumers?
The data to transform innovation to be more consumer-centric, efficient, and effective is out there. It’s speaking volumes. Are you listening?
Q&A with Zoher Karu, VP & Chief Data Officer, eBay
What are the most significant ways in which eBay is using analytics to gain a competitive advantage?
With over 160 million active buyers, eBay has one of the richest global datasets in the industry. We are actively working to use our data not just to explain performance, but to actively use real-time machine-learning approaches to harness the variety, velocity, and volume of data we process daily. We strive to constantly refine our interactions with every one of our customers at every touchpoint to create fully dynamic, relevant, and engaging site and app experiences, outbound marketing and contact-center interactions.
Your team uses data to get a deep understanding of consumers and consumer DNA. To accomplish this, you have needed to combine a lot of conventional data with more unconventional data. Can you provide some examples of how you have done this and what the impact has been – on consumers? And, on eBay’s business?
The more aspects of a consumers’ behavior you can bring together, the more powerful the analysis becomes, which results in a more relevant and inspiring experience. Transaction data is the basic starting point, but individual data, both historical and in real-time—from search strings, clickstream paths, contact-center interactions, buyer-seller communications, physical location, off-site internet interactions, type of device used, outbound marketing responses, and even local weather conditions—can all play a role. eBay continues to see a significant business impact by taking a more customer-centric approach to running the world’s largest marketplace.
According to Gartner by the end of this year, $2B of online purchases would have been made via a mobile device. What are the differences you are seeing between a mobile shopper and someone shopping from a laptop or PC?
The line between mobile and desktop is becoming increasingly blurred. While mobile users often have shorter, more frequent interactions, eBay nevertheless still sees more than half of our gross merchandise sales occurring from mobile devices. For example, in the US, a woman’s handbag is purchased every 10 seconds from a mobile device on eBay.
Retailers are “sitting” on so much consumer data, but many are not using it in a game-changing way. Why do you think that is?
Success does not come from collecting data for data’s sake. Acting on data requires an active combination of business thinking, analytical thinking, and technology thinking to be able to repeatedly make good decisions based on analysis and to do it at scale. Most organizations still approach data as a function of just one part of the organization, not one that is fully integrated into ongoing decision-making.
Where do you see the greatest opportunity for data to play a transformative role, either at eBay, or within the greater retail industry, in the near future?
Real-time processing & deep learning/AI are two big industry trends that eBay is taking advantage of to drive growth. The continued explosion of the variety and velocity of data demands real-time analysis in order to maintain maximum relevancy for our customers, both buyers and sellers. And of course, the number of data sources coupled with continued advances in computing power are enabling rich applications in the world of machine-assisted or machine-learned applications, e.g. suggested pricing on products, most relevant personalized deals, or identifying and merchandising inspirational products that engage users at different points in their shopping journey.
What is the best piece of advice you’d give companies seeking to maximize the value of their data?
Data is not just the job of the IT department; an analytical orientation must permeate all groups and aspects of the company. Data must become integral to both real-time operational and strategic decision-making processes, not something that you look only periodically to explain your results.
Disclaimer: Zoher Karu is a member of LatentView’s Customer Council
LatentView was a proud sponsor of the 3rd Annual Retail & Consumer Packaged Goods Executive Summit held in May. The two-day event brought together leaders in the retail and CPG industry and the conversations were thoughtful and illuminating – and underscored 10 key truths that every retailer needs to know and focus on in 2016.
1. Everyone owns the customer experience. It doesn’t matter what your title or job description is, if the customer experience isn’t part of your strategy and everyday execution, your organization as a whole will miss the mark.
2. The customer experience is everywhere. Online or in-store. Point of sale or customer service. Every touch point creates an interaction and you need to understand each and every one of those experiences to satisfy customers and build lifetime loyalty.
3. You need to understand consumers’ natural behaviors to create the best possible experience for them. Surveys, research, and focus groups are great, but the most authentic and unbiased input you can get is from observing customers “in the wild.”
4. Innovations must be customer-driven. The consumer is in the driver’s seat. They are smart, demanding, and vocal. Any innovations that aren’t based on their wants and needs will fall flat.
5. Innovations must be data-driven. Guesswork doesn’t cut it. Surveys and focus groups give you input, but on a limited basis. You need consumer data at scale to have a full and accurate picture of market trends and gaps.
6. Innovation must happen as close to real-time as possible. Fail-fast and first-to-market require agility and rapid response.
7. CPG brands and retail outlets must collaborate. CPG brands and retail outlets are dependent on each other for mutual success. True collaboration, which can fuel game-changing innovation, requires transparency.
8. True one-to-one messaging is the goal, but not yet a reality. Innovation isn’t just for product and experience development, but can improve campaigns and communications as well.
9. Think beyond your typical channels. Expand your reach by building connections with influencers and creating relationships outside traditional partnerships.
10. Consistency is critical. Consistency means much more than omnichannel. Yes, you need to provide a consistent experience across channels. But you also need to provide consistency of experience over time, and this includes ensuring that innovations align with consumer expectations and your brand promise.