Archive for category Big Data Leadership
Often experts pit Big Data and market research (MR) against each other, as if Big Data will make MR increasingly irrelevant. It’s a misguided match-up. Recently, At AbsolutData, in my capacity as SVP, Analytics, I had a discussion with a CPG senior executive in charge of insights, market research, for a large international CPG company. He is vitally interested, as with many leaders in the industry, in how to best combine Big Data with market research insights: Finding the best ways of bringing the two together.
I’ve taken the liberty of organizing that discussion into a Q&A format to bring out the most useful points. The discussion addresses common concerns about types of Big Data and illustrates high impact opportunities for it when combined with MR.
Question 1: How representative is Big Data, particularly for categories like kids yogurt where the core target is mothers who may or may not be the people expressing opinion/views on social media, chats, blogs etc?
Big Data can quite well represent a population. On a high level, some countries, like the US, have very high internet penetration and social media use by moms, suggesting that geographic internet penetration is a factor in determining degree of representation for given populations. Having said that, extent of representation is not a one-size-fits-all concept but is applied differently based on the context. So to more fully address the issue, we need to step back and put this question into the context of the intended use of the Big Data –
- Will we be trying to forecast demand?
- Assess overall brand or product satisfaction?
- Decide to make a product or a product line change?
- Understand the market-wide impact of product or advertising changes?
- Trigger more in depth research activity?
- Understand trends?
- Clarify consumer language and perception, etc?
There are many potential uses of Big Data and depending on the kind of Big Data we are referring to, it can have an impact on each of these areas.
The extent of representation depends on the kind of Big Data and the brand category. Furthermore the appropriateness of a given level of representativeness depends on the application of the Big Data.
If we define Big Data as Social Media – which includes tweets, FB comments and likes, Pintrest, Instagram, blogs, reviews, chats etc. these kinds of data will be representative of a given kind of population. For a category like kids yogurt, customers who mention the category or specific brands will be those who are involved in the category and are likely heavy users. These people are also most likely influencers in their circles of friends.
In fact, these people would well represent a valid target for the purposes of such issues as
- Assessing brand image
- Success or problems with product changes
- Tracking or anticipating extremes of satisfaction (which really is the zone of interest when examining satisfaction over the longer term)
- Exploring usage occasions, and
- Identifying most compelling features and benefits for communications
As another example, innovative companies like BlueFin Labs (recently acquired by Twitter) have shown a direct correlation between twitter comments and advertising effectiveness when correlated in real time with video streams in TV.
The application has a bearing on the appropriateness of representation – but the kind of data also bears upon the issue of representativeness. As detailed below in response to the second question, Big Data means more than Social Media and the data has different properties of representativeness, especially when integrated with disparate data sources.
Question 2: Therefore where and how can Big Data be used? And where can it not be used? What, if any, are its limitations?
In addition to the Social Media aspect of Big Data, and its associated specific uses, broader use cases become relevant when considering aspects of Big Data. We often think only in terms of social or web activity but we can include other data streams such as
- Daily or hourly sales by location
- Stock out information and distribution information
- Temperature and weather
- Socio-psycho-demographic information based on location
- Daily information on promotions, coupons, mailers, etc.
Because of the velocity, resulting volume, and variety of these kinds of data they can be used in solutions which are particular to what we consider “Big Data” these days. Not only can we map, in real time, these different elements together but we can model them as well to create early warning systems, recommendation systems, and communications and operations real time interventions. Social Media monitoring with text analytics can be overlaid geographically against sales, promotion usage and distribution information to react in real time with retail partners on pricing decisions or display options. We can then include demographic and even psychographic zip+4 information, and traffic information to maximize sales or communications effectiveness.
In short, when taken in its entirety and integrated together, the different aspects of Big Data that have varying degrees of representativeness when individually applied can be blended for general representativeness and applied innovatively to very important decisions. And, innovation is the fertile ground of competitive advantage.
Big Data can further be applied when approached more assertively. Big Data is a flow of information. It is somewhat reactive (but there is more to be said about this aspect) and, if we think in terms of social media, not particularly specific in all the ways we would prefer.
It must be harvested, filtered and interpreted. Or it can be blended with more traditional data to imbue more meaning.
If you are looking to test specific ideas, new product concepts, advertising copy, new product line configuration or pricing, then Big Data generally, used passively, will not provide the answers.
If you want to dive deeply into your target market to connect lifestyles, behaviors, attitudes and needs for segmentation purposes on a strategic level, you will need more than what is commonly available in Big Data.
This does bring up an interesting point, that of using Big Data passively or more actively creating data flows to more directly address specific issues.
On a very high level this is what we do with A/B testing of offers, promotions, web site enhancements, etc. We actually put something out there that customers react to in their normal activity and we measure those reactions and mine them for insights.
Likewise in the Social Media sphere much can be done to elicit more targeted and in depth information. Contests, viral announcements, online events, and such can be used to find out what people think or how they feel about very specific kinds of issues, products, features or benefits.
Question 3: How can one integrate Big Data with primary data and what would be its applicability?
Many feel very excited about the combination of Big Data with primary data.
On one level the excitement stems from the promise of enriching Big Data flows with the intimate and more detailed information that comes from primary data.
And vice-versa, the ability to further enrich our primary data insights by appending or blending in associated Big Data information.
There are many levels of integration that are possible now and yet will be possible in the near future. Here are a few examples:
- Augmenting a large portion of brand and advertising effectiveness tracking with social media – the best solution is a blended solution now
- Sampling for primary research from people who are part of a multi-device panel so that we know their primary data as well as their web and social interactions across a period of time
- Sampling for primary research directly from social media platforms, and therefore being able to tie into that users past data on that site and maybe even be able to glean information from their social-graph
- It is more typical to use social media listening and text analytics to identify trends, problems, and opportunities that can then be further explored using the more precise tools of primary data collection.
More specifically, AbsolutData has created several tools that specifically link Big Data and primary data. We can:
- Mine the social sphere to come up with an initial set of brands, attributes and levels to offer the most realistic testing using conjoint
- Track brands allowing the issues and image items we are tracking to take form from what people are talking about
- Develop a more realistic set of brand driver models based on social media information
- Administer surveys on consumer’s mobile devices allowing us to tie into social and local data both parts of the Big Data information flow.
In short the main areas that currently can blend Big and primary data would include
- Product development
- Advertising and promotional testing and effectiveness
- Segmentation and
- U&A type landscape work.
We can further combine primary data with other kinds of geo-demographic Big Data which could also include data on sales, inventory, pricing, promotion and distribution to more fully explain patterns observed in the primary data. In this area we have specific experience in
- Combining transactional data with behavioral data from survey for a segmentation exercise undertaken for a leading US based sporting goods retailer
- Combining internal CRM data with survey data to predict travel wallet share for a leading global hospitality major
- And other studies for our global clients
These ideas represent some of the increasing ways that Big Data and MR work hand in hand to generate more valuable insights. The two types of data do not contend for the insights but complement each other in deepening those insights. Each brings its own critical elements of value.
We love search, but do we really? What we are really saying is: I wish someone would find what I want. When we make people search, are we failing?
I am cross-posting a blog entry I authored for my company, AbsolutData, this month entitled: Identifying Customer Needs with Big Data.
“By the time you search, something’s already failed,” Phil Libin, chief executive of Evernote, said in an interview recently with the New York Times.
Brands can’t afford to fail to anticipate needs. With customer interaction, brands must respond before being explicitly asked – and the window of anticipatory response is shrinking every day. Before customers put energy into searching the site or asking for a product, brands that win in this ultra-competitive environment will have already anticipated – and created – a solution. As marketers, if we want to lead, we must provide customers with a situation that cuts out uncertainty.
Most businesses think this is rocket science, but we’ve had the tools to do it for a long time. Today it’s getting easier and becoming more of a mandate with the benefits of Big Data.
Big Data is already being used to identify customer needs
For 60 years, marketers have been building products that anticipate the needs of consumers. The difference now is that we have the data and tools to understand these needs in various contexts. Customers have been segmented by groups for more than 10 years, but Big Data allows us to classify individual customer needs and act in real time.
What used to be one-way is now a two-way social channel. Digitally and socially, marketers have new opportunities for relationships to increase loyalty through trust and wonder.
Big Data also allows for geographic opportunities to anticipate customer needs. Whether they’re on the highway, visiting a shopping center or out to dinner, we have an opportunity to communicate with customers at virtually any time using a mobile device. No, it’s not rocket science. In fact, some companies are already using analytics to anticipate their customers’ next moves.
Google Now’s Google Cards are a great example of how data is already being used to understand customers’ needs before they search. Google Cards works on Android phones to search existing user content, in addition to other data patterns including geographic location, to anticipate what the user will need.
For example, when your alarm goes off in the morning the application pulls up directions with the approximate time your route will take, suggesting when to leave and even providing alternative routes, if traffic is heavy. Google knows you go to work at the same time from your appointments and knows the location from your past map searches. It will even tell you stop hitting snooze or you’ll be late! The application also pulls up other things you may need for the day, including weather and important appointments.
A wider known example is Expedia’s use of data to suggest other items you may be interested in booking based on your previous behavior and others who have taken a similar trip. Even Amazon suggests new books, based on your interests and what others have read.
How businesses can move this forward within their organization
Most companies already have what they need, but they’re either not capturing it or aren’t using it in the correct way. Businesses have access to customer behavior data as well as social and transactional data. This gives them the tools to understand their customers on a much deeper level.
The first step in using this data is figuring out the vision – then you can make an argument for collecting, aggregating, storing and even mining the data. This comes from thinking about the company over a transformative period. How do you want the customer experience to differ? How can you improve the business? Larger, strategic changes can be greatly improved by the use of data.
Once you’re ready to integrate customer data, take it a step further and ask for feedback. Then, learn from behavior and refine the process accordingly to create an assertive interaction.
The tools you need and the team you’ll build depends on your strategy. There are a number of data platforms that allow for easy data access and management. New flexible analytics workspaces allow you to discover data and patterns you might not have known are important.
Finally, having the right talent is an imperative part of the process. A third-party analytics team, such as AbsolutData, can provide additional talent and tools support, or even take on projects externally.
How will this be used in the future?
The important thing to remember is that this is already happening now as the leading edge of current applications.
I don’t think we’re necessarily at the point where it’s a failure to have a customer search for a product on a brand’s website – but that’s where it’s headed, and quickly. Businesses must rise to overcome this statement of failure.
The cold, hard facts of life in this accelerating sphere of customer interaction show that if you are not taking steps now to learn and engage more, you will likely find yourself behind the pack. On the other hand, there is ample opportunity to get in the game or deepen the efforts now and doing so will pay handsome dividends.
The times are changing and changing fast. Just like the data for these times. It comes fast. It changes often. This opens up new opportunities for savvy marketers. However, these kinds of opportunities need a structural and organizational mandate. These opportunities are big and the best competitors are capitalizing on them. What are the opportunities? Real-time automated customer interaction — customized customer interaction. That’s a mouth full but very meaningful — real-time automated customized customer interaction.
This is the peak of the big data mountain value. Its a mountain and that’s why it takes serious organizational commitment. Building or moving mountains. First, you need the vision of where you want to go. Then you have to make the mountain. That involves getting your data assets and resources properly organized. Then you need an infrastructure of BI and reporting to glean low hanging fruit value and initiate best practices organization governance with the right information flows. Then you take this data further through exploration and analytics. Finally you apply those analytics into your automated, customized framework.
In the ideal world — you can piece it together like this. But more likely than not, you must work in the context of legacy structures. You must incrementally build. You must generate short term wins, rally the troops, generate greater commitment and build into a vision. This is where the rubber meets the road for most of the folks who dive into big data.
Its a constantly evolving world with new tools and technologies. Its a challenging world where intellectual resources and manpower is constrained. Its a world where going it alone has some serious risks — risks of doing it with less effectiveness but also risks of not getting it done at all.
This article, authored by me, at CMO.COM, motivates the need to be on this journey and to plan your partners carefully.
Mobile apps will rock your marketing and your marketing analytics world. If they haven’t done so already or are not in process — you are behind. And, if you are behind, unfortunately you are not alone. I have talked before about assertively creating your own intelligent anlytics leadership opportunities. I have talked about actively influencing your data streams and not just being a passive recipient. Mobile apps drive active management and enable you to create rich opportunities. However, the opportunity only comes when the apps are used and when they are coded in a way to collect not only direct but indirect data.
In a recent article I was quoted about how to develop an impactful mobile app.
“The app must provide value. It must link with other apps … It needs to be social and it should be gamified … So the first thing marketers need to do is develop and deploy custom, linked, value-providing apps.”
In addition to building your own app, which gives you the greatest degree of flexibility and interaction, you will increasingly be able to take advantage of location-based app platforms — like the recently released Facebook Home. These kinds of apps will limit your flexibility, but probably give you much better access to non- or infrequent customers giving you a fresh customer acquisition channel among those who would not have your app.
So, where is your organization? Do you have an app? How well is it being used or shared? How well is it targeted? Was it developed with a data strategy in mind? How are you using the data that it generates? Have you linked it to other data streams? What solutions are you producing from it? What value do you add through the data it generates? Have you looked for other location-based app platforms you can leverage?
Answer these questions well and you are ahead of the game. If not, use them to guide your location-based app strategy, development, or refinement.
In addition to quick service, QSR has quick data and a lot of it. They also have the advantage of frequent interactions with customers and with the same customer. This adds up to a huge Big Data opportunity. However, many QSR companies are not very analytically oriented or they have complicating franchisee relationships to navigate for investment in data systems and analytics. The article linked below comes, in part, from an interview I had with John Morell — who was very interested in exploring the role of Big Data in QSR. He is finding that Big Data best practices and applications, while being applied well in pockets, still has a lot of opportunity in QSR.
McDonalds is exploring social media in greater depth. Dominos wowed the pizza ordering world with their online information system that lets users track pizza making progress. Taco Bell is working wonders operationally with their inventory management systems. Even given these pockets of application, few, yet, are harnessing the power of the data they have at their fingertips. What can they do?
Here is a quick list of “No Brainer” ideas. They are “No Brainers” because not because they are obvious nor because they are simple. They will all lead to increased revenues and generate a high ROI both in the short and long term if approached right.
- Start or deepen loyalty programs and combine with social, apps and gamification elements to generate rich data — so much opportunity here to create value
- Organize individual level purchase data and combine with digital, social and media information for rich data — the basis for real time interaction with customers
- Combine unit performance data trade-area geo-demo-psycho information, social, individual level purchase and loyalty data — for much more effective targeting
- Correctly attribute sales to online, digital and offline activities and tie this attribution to the individual level for more powerful cross-channel integration
- Build recommendation systems for promotion customization, loyalty deepening, word of mouth generation, online ordering, in-person prompting
- Optimize inventory systems to decrease costs and increase quality
- Customize local menu options for increased sales
- Track and respond to customer service issues before they become big issues — by area; curate your brand
- Optimize your online and offline media spending and efforts for better ROI and increased sales
And there are many more Big Data opportunities and priorities. When considering Big Data investment, especially for QSR, here are a few guidelines:
- Always include short term (1-3 month) wins that can be used to generate momentum
- Define win in terms of ROI or even direct impact on stakeholder decisions or KPIs
- Put in place a larger vision and plan that can motivate deep emotional commitment and advocacy
The key is to start down the path and begin to gain experience and get your head in the game. Folks who keep putting it off, at this point, are at risk of just being in a different league of success than their competitors who are trying to play in this arena. While these are not quite “No Brainers” by some definitions, they should be if they were on your radar.
“The big data process is built on the pillars of measurement, experiment, analyze and replicate, contends Brynjolfsson. The continuous implementation of that cycle based on real-time data will strand less data-oriented competitors.”
In a fast moving competitive world, “stranding” a competitor means winning the prize.
Connect with your customers. This article extends a theme that has become even more important as we need to prioritize Big Data efforts. Three “missed” opportunities are detailed in the article by TMCnet (http://buff.ly/ZfYllU). These opportunities include 1) connecting with customers immediately, 2) providing customized opportunities and 3) assertively creating new data. These are areas almost all companies should leverage more.
AbsolutData: Missed Opportunities in Big Data CRM
March 20, 2013
Three Big Data Opportunities Your CRM System is Missing
Social media and the Internet deliver data that offers added understanding to engage one another in real time, leading to richer emotional connections with customers. Although many customer relationship management practices involve mining big data, most regularly overlook powerful opportunities that can improve customer relationships and loyalty, and increase revenue.
The three most overlooked opportunities in big data CRM stem from lack of interaction on three different levels:
1. Connecting with Customers Immediately
Social media allows brands to see what customers are talking about in real-time. Consequently, one of the biggest missed opportunities in CRM comes from brands’ inability to act simultaneously with customers. Many companies are managed so tightly, they can’t respond fast enough.
Providing a structure for timely, relevant interactions and conversations with customers allows brands to use CRM systems more effectively. Algorithms and flexible plans for responding can help brands thrive in real-time interaction.
2. Providing customized opportunities – “These guys really ‘get’ me”
Big data allows us to listen and understand customers on a personal level. However, a missed opportunity comes from our inability to provide customized engagement to forge stronger, personal relationships.
While many brands are well-equipped to scan the airwaves for concerns or issues, and respond accordingly, they’re missing a crucial piece of the pie: the chance to engage customers already satisfied with the brand on a personal level. Brands need to have listening systems in place to engage with happy customers too. By offering customized rewards and opportunities, customers feel valued, and consequently, more attached to the brand.
3. Assertively creating new data
Brands typically take a passive role in measurement. Websites or other typical touchpoints are designed generically from a process standpoint and typically not from an information purposing standpoint. When developing a presence on new platforms or even re-examining their existing ones, brands should set up measurements for engagement ahead of time with an eye toward shaping the kind of resulting information and feedback.
The saying, ‘build your network before you need it,’ applies to social media and customer relationships just as much as anything else. Purposeful measurement of interaction is an opportunity many brands can further leverage in big data management.
Although big data can be intimidating, it drives real business through smart CRM initiatives. It can drive even more value when its unique characteristics are leveraged. With the right plan, skills and technology in place, companies can apply real-time data to their CRM practices and build stronger bonds with customers – closing the gap on missed opportunities.
Chris Diener is senior vice president of analytics at AbsolutData, a global analytics and research firm based in San Francisco.
Edited by Braden Becker
If you are in market research (MR), you may debate the which is tortoise and which is hare, but you probably do see a race and competition for the prize. Or, more realistically, you may fear that others see it as a competition. The prize may be budget, recognition as the source of valuable customer insights, or winning jobs. This may be a limiting perspective. If you are in the Big Data space could see it as a race but MR is probably not on your radar or you consider the outcome a foregone conclusion. You probably believe that MR could be a valuable appendage but ultimately discount MR when viewing the main role and impact of Big Data. There may be some blind spots. However, the two areas do overlap and treating it emotionally or functionally as a competitive situation will be counter-productive and lead to lost opportunity.
Some say that focus group and survey-based market research has reached the end of its life-cycle and will be replaced by Big Data analytics. Those who fully promote this position usually have an agenda to push or they are not well informed about market research. However, it does seem that these two areas of market intelligence, customer insight, and decision support both offer valuable and maybe even critical decision support. The million, or billion, dollar question is how to integrate them and leverage them together. Will this integration be the basis for the next round of killer market research applications or the means of getting the most value from your big data investments? My thoughts in this area were triggered by the linked article that contains some comments, including one application that combines the two areas of MR and Big Data.
In both areas, those who wish to capitalize the most will figure out how to combine them to leverage value. In Big Data, folks will come to realize that there are many holes in the data, many missing links, many areas where current or past data will not suffice in predicting the future, and many areas where the value of the insights can be magnified through the leaven of custom MR.
On the other hand, MR folks either have or will come to recognize that the in-context reality and pure organic validity of Big Data adds immensely to the precision inquiry possible in MR. Big Data integration will increase MR value by bridge limitations or biases created by interviewer/researcher subjectivity, sampling frames, respondent behavioral self-report inaccuracies, perceived insights credibility, perception of frequent lack of tangible real-world action-ability, to name a few.
Seeing or specifically identifying the areas of combined value will be the first and most important step to making this happen. The next step, and arguably the more difficult one, is to actually combine the functions related to Big Data and MR to bring about a profitable union. Most corporations have these functions silo-ed separately, based on their historical origins and historically separate functions, skill-sets and processes. In fact it goes even deeper in that these two areas may compete for the same budget and in fact may feel quite competitive with each other for recognition in the organization. Executives not only have an organizational issue to resolve but likely an emotional one as well.
On the supplier side the difficulties are multiple for being able to combine these areas. First is that most MR firms do not have an understanding of or a captive skill set that encompasses Big Data analytics. They don’t have the mindset for what it is, how to get it, and what to do with it. Next, most emerging Big Data firms come from a programming, IT, data mining, or highly technical area of strength. They do not have the insights background or training and skills. The foundations of marketing strategy and consumer behavior and psychology is lighter than it needs to be. The softer, more granular or individually based understanding of motivations, attitudes, drivers, etc. and how they mix together to generate action and commitment are usually missing with folks who have been devoted to algorithmic programming or econometric modeling and mining.
The future in this area belongs to people and organization who can bridge these domains. This is a process and the sooner it begins the better and the more likely you can capitalize on it for competitive advantage. Identify the needs, find the relevant gaps in addressing the needs, prioritize and act on filling the holes and showing tangible progress through a series of quick wins.
To motivate these changes, we need to see more examples of how the two domains combine for added value. The referenced article (noted below) provides one lightly sprinkled example. I believe we will begin seeing more.
How will it play out? Will Big Data analytics be the hare? Will the hare win? Maybe fundamentally, is there a race? Maybe, we see the races either being set up or already in process and the question is how do we change the nature of the race so that instead of competitors, the tortoise and the hare are teammates?
Referenced article: http://smartdatacollective.com/sandrosaitta/94181/guest-post-mark-zielinski
“The general consensus seems to be that “big data” is never going to replace traditional research – that is, specific research methods like surveys and focus groups that deal with particular topics will always be around. These specific research methods answer the question of “what” – that is, they are concerned with empirical details. For example, an online survey may indicate that compared to 2011, this year 5% of soda drinkers no longer drink Coca-Cola on a regular basis. Where “big data” aims to change research is in the “why” – that is, the broad trends and underlying reasons why certain results have been obtained. Using our previous example, “big data” may be able to tell us that key nutritional influencers have recently been saying that carbonated sugary drinks reduce life expectancy by an average of 4 years in healthy individuals. By having both the “what” results and the “why” results, researchers can use this combination of data to have a much clearer picture of a particular situation, and potentially be able to advise their clients on how to act to obtain the results they wish to achieve.”
Big Data Report: Computer News Middle East Article.
Its an exciting time. But it shouldn’t be intimidating nor paralyzing. Through a series of reports, Gartner and others continue to highlight the business impact of the growing big data phenomena. Leaders should use these kinds of articles as a call to action to examine their own companies and set in motion the processes to ensure they are best positioned for the coming years. For many firms this means outsourcing the necessary resources as the company moves along the learning curve, scopes out the opportunity and generates success through a series of pilot efforts.