If you work in a B2B company and would like to improve the quality of your sales and marketing data, you might want to consider how a few companies are using data to disrupt markets. In the process, you may be able to use these examples to help executive leadership in your company commit the necessary resources to lasting improvements in B2B data quality.
B2B Lessons from Leading B2C Companies
I know when people mention what business-to-consumer (B2C) companies are doing, my alarms often go off. The volumes of data, the problems, and many other considerations are quite different from those we face in B2B. That said, B2C companies are often more advanced than B2B companies, in terms of marketing practices and so the right examples can be illuminating.
Companies like Amazon and Netflix have greatly disrupted corporate giants like Sears and Blockbuster. Other B2C companies like Uber and Airbnb are disrupting taxi and hotel industries. In one sense these companies are in the ecommerce, social media, taxi, and hotel businesses. In another, they are in the data business. Through hyper-relevance, each of these companies has transformed the experience for their customers. Ordering products (Amazon), watching movies and TV shows (Netflix), getting a ride (Uber), and renting a place to stay for a few days (Airbnb) is better, faster, and cheaper, and data is a big factor in making these disruptive companies successful.
The Data Agility of Amazon
Amazon has made hundreds and often thousands of reviews available on most products, searchable by level of satisfaction. Amazon also makes very logical suggestions on what you might want to buy next. These are just two examples of the power of data. This hyper-relevance allows Amazon to send more delivered and opened emails than any company in the world, according to eDataSource, which tracks billions of emails as a service to email marketers. For example, between June 29 and July 9, 2017 to promote Amazon “Prime Day,” the company executed 181 Prime Day campaigns, totaling an estimated 179 million emails, with read (open) rates averaging an awesome 24 percent.
The Data Daredevils at Netflix
Where renting a movie had once been cumbersome and fraught with late fees, now you can watch lots of movies without going anywhere and at a much lower price. Plus, Netflix will suggest relevant movies based upon what you have watched and liked so far. This pleasurable customer experience didn’t just happen. It took a relentless commitment to data, one that has paid off not just in improved consumer experience but also in original content creation. Netflix, which was founded in 2007, took six years to collect enough data to predict the success of its first original production, “House of Cards.” This use of data has resulted in Netflix having an 80 percent hit rate on its original shows versus a 30-40 percent hit rate with traditional TV shows.
With Uber, you can get a ride in a few minutes and pay less, rather than getting soaked in the rain while trying to flag down a taxi. The whole time, you can see where the driver is, what kind of car he or she is driving, and the approximate arrival time. You don’t have to exchange money with the driver, either. The improved “taxi” experience is made possible because of data.
Uber stores massive data about its drivers. As soon as you request a car, in fifteen seconds or less the Uber algorithm matches you to the closest driver. Meanwhile, Uber stores data on every trip you and the driver take. Uber then uses this data to predict supply and demand and to set fares. The algorithms also consider traffic flow in each city at different times, considering things like bottlenecks and accidents.
Eleven Petabytes of Data at Airbnb
With Airbnb, you can read reviews of each place, view options on a map adjacent to where your meetings are, filter your search on a variety of criteria, give your host specific feedback after your stay, and so on. These digital experiences are all driven by hyper-personalized use of data.
Like Uber, Airbnb has created a marketplace, not between car owners and passengers but between property owners and renters. In this context, Airbnb isn’t just renting places to stay, it’s looking for a good match between the property owner and the renter. To do so, Airbnb considers the preferences of the host, looking at four vital areas to influence the guest: behavioral data from the Airbnb website, and dimension factors like device, language and location preferences. It even takes into account sentiment like customer reviews and survey results, and imputed data. This underlying big data platform has encouraged property owners to join the Airbnb community, increasing the appeal to more travelers.
The Google B2B Big Data Tsunami
B2B companies have experienced similar disruptions as these B2C businesses. Consider the Google search business. Google probably has more data than any other B2B company. If you don’t think so, ask a friend to conduct this experiment. First, make sure both of you allow Google to know your location. Next, you both type in the exact same search phrase in your own cell phones. Chances are, you’ll each get different search results relevant only to you, even though the search string is the same on both phones and you’re sitting next to each other.
Google isn’t just doing what you ask; it’s looking at everything it knows about you and delivers what it thinks you mean. (I have good friends who aren’t that empathetic.) This database expertise also delivers value to the advertiser.
Before Google, companies lacked the ability to target ads based upon the immediate questions someone had. Very quickly, massive dollars that companies had previously spent on traditional media began to shift to AdWords. And just as quickly, newspapers and magazine print revenues declined precipitously. Consider this graphic on the US newspaper industry as Exhibit A, from Pew Research Center.
Think of Your B2B Data as an Asset on Your Balance Sheet
For companies who want to disrupt industries, as these companies are doing, the first step is to begin thinking of data as an asset on the balance sheet. If this seems far-fetched, consider this: Gartner predicts that by 2022, companies will be valued on their information portfolios. In fact, a Gartner study found that “companies demonstrating "information-savvy" behavior — such as hiring a chief data officer, forming data science teams and engaging in enterprise information governance — command market-to-book ratios well above the market.”
Think of data as an asset your company will need to invest in to harvest the highest return on investment. Technologies like machine learning and artificial intelligence are only effective when fed reliable data. To that end, your company needs to take steps to gain as much advantage as possible from your data and the newest technologies you can apply to it.
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