Big Data analysis can improve sales through honing the multichannel experience, cross-selling, placement optimization, location-based selling, assortment optimization, and in-store behavior analysis. The book, Sales Growth: Five Proven Strategiesfrom the World’s Sales Leaders, reports sales gains in each of these areas by about 2 to 8 percent; gains in each of the areas combined are likely to increase margins between 8 and 25 percent.
Some companies even use their data to fill a new service niche. For these companies, Big Data has become the most valuable raw material of the new millennium. Of companies with optimal data strategies 60 percent are already generating income from the data they gather and analyze and over two-thirds (69 percent) of the executives surveyed felt that there is a case for starting a new business unit dedicated to developing data-related products or services.
Unfortunately, many companies lack a data strategy. Even those companies who do have a data strategy could improve the use of their data assets. According to a study of over 1,600 businesses, an astounding 76 percent of businesses lack the understanding of the potential value of their information and therefore had not invested in the correct data management platforms and software to help them use their data strategically. Only 4 percent of the businesses surveyed had data strategies and governance measures in place to realize the commercial and operational benefits of their data.
If it seems like the Big Data rush has left you behind, you’re not alone. But don’t worry, there’s time to catch up.
In short, a data strategy is the ability to use data analytics to meet company goals. A data strategy should only be employed after company objectives have been outlined with the entire company trained in data accuracy and protection. The importance of the data strategy should be made clear: data is essential to target customers and potential customers in helping to customize their experience with your brand. Data strategy helps pinpoint where a customer or prospective client is in their purchasing journey and help them in their decision-making process.
Used strategically, Big Data can help companies better understand their customers, predict preferences, offer product suggestions, and create a 1:1 personalized decision-making purchasing journey.
Because patterns of consumer habits are essential in using Big Data as a predictor for marketing strategies, it is important to understand a little about the psychology of behavior and behavior modification. Simply put, people are creatures of habit. Charles Duhigg, author of The Power of Habit: Why We Do What We Do in Life and Business, has studied habits and their modifications extensively. In a New York Times Magazine article, he cites a Duke study claiming that 45 percent of the choices we make are shaped by habit rather than conscious, purposeful choices.
Simple actions such as backing out a car, boarding an airplane, or tying a shoelace, among a vast array of others are actually a more complicated series of events. When smaller actions (make a ‘bunny ear’ with your shoelace) are linked with other actions (the other lace goes around, up, and through the hole to tie your shoe), this set of behaviors becomes solidified as a sequence. The action of grouping small actions together is known as chunking, take all those small chunks and you begin to build sequences that become habits.
These chunked habits are particularly important for businesses to understand their customers. Once they can figure out the sub actions causing the larger action they can begin to strategically target the way they interact with consumers.
All habits--including purchasing habits--can be modified. Alan Andreason’s UCLA study found that routine purchases can be interrupted by lifestyle changes. Moving to a new house may change preference in grocery stores due to proximity. A divorce may trigger the purchase of a different brand of laundry soap, pregnancy may change a woman’s preference to frequent multiple stores in exchange for the convenience of a one-stop shop.
The principle of disruption also applies to B2B operations. A merger, a restructuring in management, an expired URL, a newly patented product, application for zoning permit, or a newly licensed business may all signal disruptive instances where your business could provide a useful service.
A data strategy that utilizes Big Data by combining multiple data sets for insights and actionable steps can keep you ahead of the competition--and perhaps even your customers themselves.
And that’s where data management becomes essential.
Being able to collect and acquire data is one thing but just because you can doesn’t mean you should, just yet. Don’t jump on board the Big Data train without a fully thought out plan.
If you are lacking a data strategy, here are 4 steps to consider:
1. Determine your company goals
The first step to formulating a data strategy is to look at your company goals. Forget about Big Data for a bit and focus on your overall objectives. We’ll assume, of course, you want to increase your bottom line. But how, specifically? What are your desires for your company? Do you want to--
- expand into a new or international market?
- introduce a new product or service to existing customers?
- capture a market segment?
- target prospect online?
- Interact with a specific demographic before your competition does?
By first defining your goals, you’ll be able to step back and see how a data strategy can help you.
2. Identify useful data
After your goals are clear, then it’s time to figure out how Big Data can help your company reach those goals. You may already have names and emails, now it’s time to decide which other data sets would be useful to crosslist with the existing data. Things such as time spent on your web page, information downloaded, or abandoned shopping carts might be useful information to collect internally.
The important strategy here is to know what you want to do as a company and then pinpoint the data that can help you reach the goal. For instance, say your outdoor gear website has a goal to increase sales of hammocks and you want to know which customers are most likely to be interested in a hammock. First, you may want to know which customers have purchased hammocks in the past and cross-reference that list with their reported interests from their customer profile. Analyzing this information will report correlations between hammock purchases and stated hobbies, allowing your company to create customized emails, discount offers, and shopping cart suggestions. Defining the data you want will help you customize your customers’ experience.
3. Find the disruption
Remember the concept of disruption: when habits are disrupted, behaviors are likely to change. Any disruption in potential customers’ habits is a good chance to integrate your offerings. A good data strategy will look for disruptions in usual routines: moving, pregnancy, change of jobs are excellent examples of this. B2B data strategy will look for disruptions like mergers and acquisitions, new office leases, business license or DBA filings, staff changes, or advancement in technology.
Piggybacking on these disruptions of habit, your business can be in the position to help the customer. Predictive analytics can help target potential customers. With multiple data sets and an advanced data management platform, these disruptions can be predicted and customers can be targeted with offers and information.
Advanced data analytics can identify and predict disruptions in customers’ lives with alarming accuracy. For instance, some retailers uses customer purchase records from its retail operations to predict when a customer is pregnant. Mailers, coupons, and emails can then include offers for baby products. In order to not seem presumptuous, the retailer will included other random products in the sales circulars.
4. Use the right people and the right data management software
While data is the essential building block of a business strategy, Big Data is only useful to a company when it can make sense of the data and then act upon it. That’s where the role of a data analyst, data scientist, or business intelligence developer comes in. If your company doesn’t have someone with business intelligence training or data analytics training--the skills to analyze data--it’s probably time to consider it.
Data analysts are trained to work with large sets of data, write queries, and extrapolate useful insights. These professionals are trained in both computer science and mathematics and are able to study the distribution of data, derive comparisons and relationships, and create statistical models and predictions. In other words they will be able to look through and analyze the data so that it may be presented in a comprehensible fashion.
However if you are not ready to add a data analyst or data scientist to the payroll, don’t despair: some CRM programs have robust data analytics abilities and can provide advanced insights. And when businesses use these insights, they are able to create recommendations and experiences unique to each customer. So your solid data management platform will prove to be invaluable as you begin to implement your data management practices.
In addition to having a data strategy, the ability to show that your data is secure is just as important. A lack of internal data policies puts the company at risk for data loss, security breaches, and lawsuits.
The Rand Secure Archive Data Governance Survey reported that 44 percent of the firms they interviewed had no data governance policies in place. Even if a data governance strategy is in place, it’s often not enough. Scott Ambler, of AgileData.org explains that 66 percent of development/IT teams choose to bypass the data practices.
In order to protect your company data and the data of your customers, data governance strategies must be woven into the culture of your organization. Establishing a data governance board, conducting data quality assessments, and employing data security measures are steps to ensure both the quality and safety of data. Data management practices should align with business intelligence to ensure that all data is validated and secured through consistent practices throughout your organization.
Using Big Data is a veritable goldmine in opportunity yet few businesses are taking full advantage. Once your company has a clear picture the specific sales objectives needing to be met, a data strategy can be crafted to discover customer’s needs. Data analysis through business intelligence can help in finding disruptions, changes in the habit or routine of a potential customer or client where your product or service could meet their needs.
Having the right people and tools will greatly affect your data strategy’s success. Data strategists, data analysts, and data scientists are trained with the mathematical background to combine data sets, find patterns, and recommend target groups and actions. Data management platforms and data management software helps to keep data up-to-date and in a central location.
Companies who lack a strategy for using Big Data in their marketing stand to greatly improve sales through adopting practices to help better target customers and offer a personalized purchasing experience. Likewise, data governance strategies must be in place to ensure the security of both customer and company data.