Big Data… even though that’s still a relatively new concept, it feels dated and overused in the current marketing landscape. Love the term, or hate it, big data is both the present and the future of marketing. It enables us to provide relevant information to consumers exactly when it’s needed. It can inform accurate pricing, benefiting both business and consumers. It can even be used to predict and guide consumer behavior.
The key to putting consumers at ease when using big data (especially with all the implicit risks that often come with it) is to emphasize the convenience and better experiences big data can help deliver. Here are three ways big data can help companies build a stronger connection with consumers while growing revenue:
In the same way that Netflix and Spotify can serve up movie and music recommendations within minutes of engagement, marketers are able to serve up relevant content to their consumers by tapping into data. The days of static content are gone; now consumers can browse their email, social media or any website, and see relevant content that captures their attention.
For example, Target was able to use big data to create a formula that identifies pregnant women. The brand started promoting products that a pregnant woman would use – diapers, bottles – along with random items like lawn mowers and cocktail mixers – in their targeted advertisements. This marketing tactic worked; the year Target started using their advanced data to market to pregnant women, they made $23 billion more in annual revenue, due to their “focus on items and categories that appeal to specific guest segments such as mom and baby”.
In addition, big data capabilities like smart search are making it more convenient for consumers looking for products on brand websites. Smart search improves the accuracy of site searches by analyzing previous searches, synonyms, and spelling errors to better identify what consumers are looking for. For example, Walmart added smart search to their website and increased conversion rate by 10-15% – which means millions (if not billions) of dollars of additional revenue.
A study completed in 2014 by McKinsey & Company found that 30% of the hundreds of pricing decisions businesses make every year fail to deliver the best ROI.
Brands identify the optimal price a consumer is willing to pay for a product by considering various factors: availability, market competition, demand, value of the product to the consumer, etc. For a small company, with a limited number of products, this sort of analysis is straightforward. Once we begin looking at larger businesses, the process is time-consuming and extremely complicated, often resulting in incorrectly priced products. At its base this is a big data problem, but luckily there is a big data solution. With the capability to analyze large sets of data, businesses can correctly price their products based on market fluctuations, thereby making more sales and ultimately growing their customers’ lifetime value
Sasol, a chemicals and energy group, used big data to update their pricing generating impressive results. Initially, the sales team would increase prices every year, they developed prices based on ‘old school’ logic: cost to produce the product, standard margins, prices for similar products, standard yearly hikes, etc. However, sometimes these excuses just weren’t good enough for customers: prices shouldn’t just go up because margins had to increase every year.
When they started using data analysis to make their decisions, they saw a profit-margin lift of 3% to 8% by setting prices at much more granular product levels. What this meant was that instead of raising prices based on yearly hikes, Sasol was using their data to set prices. For example, they used dynamic deal scoring which provides prices at the level of ‘…individual deals, decision-escalation points, incentives, performance scoring…’
In layman’s terms, instead of looking at the yearly revenue and transactions, Sasol decided to use smaller, individual purchases which made their price-setting more relevant to each unique customer group.
In the graph above, you can see that rather than offering the same discount to all customers, Sasol identified distinct customer segments who would receive different discounts, based on previous purchases. Each blue dot represents an individual customer who purchased at a specific price point, while each Orange dot identifies customer groups that have similar shopping patterns. By using purchase data to group customers together, Sasol was able to increase revenue by determining pricing based on their customers’ willingness to pay a specific amount.
As companies collect more data about their customers, and more importantly, their customers’ journey, brands are able to better predict consumers’ wants and needs– sometimes even before consumers themselves. While this type of predictive data analysis should be used tactfully to avoid appearing “creepy”, consumers appreciate relevant marketing.
36% of marketers are using big data to gain insights into their consumers’ journey so they can develop more relationship-driven strategies. This means big data enables them to anticipate consumers’ needs which means more opportunity to build lasting relationship and improve customer lifetime value.
For example, when Curt Hecht served as the Chief Revenue Officer of the Weather Channel he decided to use comprehensive data to enhance their marketing efforts. According to Hecht, it was time to stop selling snow shovels and umbrellas using geo-data, and instead spread their marketing efforts to other products – for example hair products that protect against heat, humidity, extreme cold and more.
The Weather Channel teamed up with Pantene and Walgreens to run a “Forecast Frizz” marketing campaign – the partnership focused on The Weather Channel’s mobile media, where consumers check the weather more often than in any other channel. Pantene sent consumers a “Haircast” based on their personal local weather: from a humid day in Chicago, to the dry heat of Phoenix. The “Haircast” also delivered a $2 coupon for the product at Walgreens. The Result? A 24% increase in sales for Pantene products in Walgreens, and a 4% sales lift across the entire hair care category at Walgreens.
And it doesn’t stop there. Predictive marketing can help with so much more than just ads – it can assist with customer acquisition, customer retention, web and search optimization, and creating customer personas.
As we move further into the era of big data, consumers and business alike will inevitably see more risks but also more benefits. There were 5 exabytes of information created by the entire world between the dawn of civilization and 2003. To put that into perspective, if 1 kilobyte is a very short story, 5 exabytes is all the words ever spoken by human beings. Amazingly, in 2011, that same amount of data was created every two days. Today, that amount of data is created within hours. And all of it can mean unparalleled opportunities for marketers and consumers alike.
Marie is the Account Manager for Lands' End, and manages all of the Lands' End accounts, including US, Japan, UK, and Europe. She is dedicated to making sure Lands' End is functioning smoothly across all platforms, and works diligently to implement new programs, processes, and data-driven strategies to ensure Lands' End is the best they can be. Marie is extremely competitive, and with her strong background in digital marketing, she'll be sure her clients are always winning. Based in Portland, OR, she loves hiking, cooking vegan, gluten free food, and perusing farmers markets for organically grown kale and tomatoes.