Less than thirty years ago, most small and medium-sized businesses knew their customers personally and had a depth of wisdom on their customers, best and worst. The local butcher knew which cuts of meat his patrons liked best. The baker knew when people came in for their bakery items. The barber knew the preferred hair cuts and shave types of his ‘regulars’ and when to expect them to arrive for their next hair cut.Between big box stores, global manufacturing and the internet, business as we knew it has transformed. However, with that transformation has arisen new transactional data, called Big Data, to help everyone across companies understand and help their customers better than ever. And it’s not just big enterprises with sophisticated IT capabilities that benefit from Big Data. Thanks to cloud-based providers—which offer access to massive amounts of data, and the ability to analyze it, without great cost—today’s small businesses can use Big Data to better understand customers, optimize operational efficiencies and better plan for the future.
Free Big Data-enabled services
You’re probably already using one or more of the many cloud services offering Big Data capabilities.
- Google Analytics, which can help you understand where your web traffic comes from, how site users behave and optimize your site to convert visitors to customers.
- Twitter, Facebook and Yelp, which can provide real-time insight into customer sentiment about your brand.
- LinkedIn, employing specific keywords to find prospects, partners or job candidates.
- QuickBooks, which offers free industry-wide data analytics.
- Government sites, to search for census, economic, or environmental or weather data.
- Public databases like CrunchBase, which offers information about the technology industry.
In all of these cases, you’re tapping into the power of Big Data.
Leveraging other sources of private Big Data services
Other providers offer specialized Big Data analytics that can give you even deeper insights—and supercharge your business.
- By analyzing everything that’s being said about your brand on social media, for instance, you can get a deeper-than-ever understanding of your market. Or combining that information with transaction data to understand how consumer sentiment actually affects your bottom line.
- By using Big Data to improve your operations—helping you optimize fleet management, for instance, or giving you a better understanding of how specific employees drive your bottom line.
- By helping you better predict demand for your products or services—and better meet that demand without overinvesting in capacity.
Cloud-based Big Data analytics providers have invested in the hardware, software and personnel you need to get the most out of your data. They can give you the insights you need to compete—without breaking the bank. The trick, of course, is knowing which providers to turn to.
“This is where HP can help,” says Weintraub. “With hundreds of cloud analytics partners, we can connect businesses with the right solution for their needs. We’re their guide through the Big Data forest.”
Looking to the future
With every passing minute, there are 98,000 more tweets, 695,000 Facebook updates, 700,000 Google searches—1,820 terabytes of new data created, in all . Old technologies can’t handle the complexity and velocity of all that data.
Enter Big Data, which will offer increasingly significant opportunities to combine structured and unstructured data—from CRM and ERP systems, video and audio, machine sensors, social media, and more—to generate unprecedented improvement in market insights, operational performance and strategic planning.
Thanks to cloud technology, even small businesses will be able to tap into and parse all that data—affordably, and in near-real time—helping them compete with even their biggest, most tech-savvy competitors.
Big Data is transforming the way companies do business. But many firms are finding that it requires significant investments of time and money before seeing the value. We believe that by providing businesses with controlled access to their data ecosystems, companies can monetize their data to help fund the longer-term Big Data journey.
This approach differs from traditional data selling – where revenues are minimal and privacy concerns significant – in three ways. First, the customer opts in to have their data shared in return for receiving something of value in return, such as personalized offers. Second, the firm that generates the data shares access in a controlled, traceable fashion. And finally, companies enhance the data provided to partners through analytics, segmentation, targeting, and other services.
For instance, the supermarket group Tesco uses a data ecosystem to monetize data from its loyalty program, the Tesco ClubCard, through its customer science subsidiary, dunnhumby. In return for opting in to ClubCard, customers receive personalized offers. Then, for a subscription fee, dunnhumby provides consumer packaged goods (CPG) suppliers with an analytics platform which they can use to query anonymized retail transaction data, understand category dynamics and develop targeting strategies. The analytics platform also controls suppliers’ access to the data (preventing direct customer contact, for instance). This model has helped dunnhumby generate annual aggregate gross billings of $500 million globally.
There are other examples, too. Merchant-funded offers provide a growing way for credit card players to monetize customer transaction data. Payments networks have begun to provide analytical services to stores and banks using their own data. And Intuit INTU +1.69%’s Mint.com serves financial data back to the customer in a useful, graphical format, an offering it monetizes through advertising.
Is a data ecosystem right for you?
Three questions can help you decide:
1. “What is my data worth?” To know, consider the following:
- Size of the ecosystem: Businesses with large, high margin suppliers or partners can generate more value from their data. For example, grocers have relatively data-starved multi-billion dollar CPG suppliers with the size and scale to pay for insights, while vertically-integrated retailers do not.
- Breadth of data: Companies with national or global scale typically have more valuable data because it is easier to establish a market view and easier to create meaningful sales lift through targeted offers. That said, data on niche segments (e.g., such as high income retirees) can also be valuable.
- Richness of data: The more companies know about their customers the better their data. Google GOOG +0.23% Ads is so profitable, for instance, because search data reveals powerful insights about customer intent-to-purchase and other factors. By contrast, low touch or low frequency sectors (say buying a car) tend to have less valuable data.
- Customer engagement and permission: The more engaged the customer is with your brand, the more valuable the data, since engagement drives receptivity and response rates.
2. “To whom is my data valuable?” This can be other players in the value chain who are disintermediated from the end customer, firms with similar target consumers, or even the customer him/herself as in the Mint.com example, A hotel chain, for instance, could monetize customer data to restaurants, retailers and tourist attractions.
3. “How should I monetize my data?” In addition to targeted offers, many firms have created (or partnered to create) insights platforms, which provide an analytical engine that companies can use to query data, create customer segmentations, or understand the relative price elasticity of one particular product over another. Data can also be monetized by providing access to the customer, through your mobile app for example.
What to keep in mind
In our experience, the companies that are most successful at monetizing their data with this approach focus on three things. First, they recognize that they are trading on the trust of their customers. They define at the highest levels what the organization is willing and not willing to do with customer data and ensure that the model is transparent and adds value. If you had to stand in front of all your customers and tell them exactly what you are doing with their data, would you be comfortable doing so? Beyond privacy concerns, being more transparent can increase customer engagement – which in turn will drive more value from your ecosystem.
Second, the best companies establish the right data governance upfront. They determine how the organization will manage conflict of interest with partners in a clear and specific manner. A retailer, for instance, may seek to drive private penetration of their brand within a category while the supplier to whom they are selling the data does not. A good governance structure is able to help establish responsibility and determine who has the authority for making decisions. We find that the best-performing governance structures on doing what is right for the customer first, as non-customer-centric behavior is generally not sustainable in the longer-term.
Finally, the best companies realize they often don’t have the core competency to develop this kind of data ecosystem infrastructure and thus they bring in specialist firms that provide analytical engines and the tools. The partnership route can be helpful especially for retailers, banks, and airlines. If you choose to go this route, it is important to have a clear understanding of the economic drivers of the relationship and articulate unambiguously the ‘data rights’ that you are granting to your partner.
Big Data will ultimately transform many industries and firms. Done properly, data ecosystems can fund the transformation, create value for your customers, and build tighter relationships with other firms and partners.
Scott Cameron, Andrew Pickersgill, Rob Turtle are partners at McKinsey & Company. Learn more about Big Data and other topics on our McKinsey onMarketing & Sales site. And please follow us on Tw