What Does Big Data Actually Mean For Marketers?
The amount of information available to digital marketing professionals continues to increase. With traditional database systems struggling to cope and media hype building, “Big Data” has become as much of a threat to some as a potential opportunity to others.
There’s still confusion and mistrust about what Big Data really is, what it can do, and how it should be incorporated into a business strategy. Let’s start with the basics.
What Is Big Data?
Big Data refers to data sets that are so large and complex they require new forms of processing, beyond the traditional. This information is usually unstructured: things like Twitter comments, video content, and recordings of call-centre conversations. Twitter processes 400 million tweets per day, so Big Data means dealing with millions or billions of records.
It’s not just volume, though. Big Data is also about versatility and speed. Indeed, when we talk about Big Data, the term also describes the actual manipulation of data from which analysts extract meaningful insights that can aid in decision-making and business strategies. And this all happens today, not a week from now.
What Do Marketers THINK It Means?
Typically, this is limited to asking: “Which keywords are driving traffic?”, or “How many leads did we generate from social media this month?”, then turning to an analytics dashboard. Fine, but this isn’t Big Data. These are just small data points that feed marketing activities.
Such questions can be answered using traditional database queries. And they don’t require high-variety data sources or unstructured information, nor do they even make it possible to answer complicated questions quickly.
Customer-related forms of Big Data include social media posts, email, surveys, customer service call transcripts, and information gathered at points of sale. An example would be a list of everyone who visited your website twice in the last month, opened more than one email, and retweeted your social content.
The challenge and opportunity of Big Data is to take this kind of knowledge and translate it into profitable action.
Master Data Management
It can be difficult to bring together the information available from so many sources in a way that benefits your business and aids your decision-making. Analytics can help identify the type of data that will be of most value: age, gender, viewing habits, how customers consume information, etc.
The process hinges on common sense and best practices – knowing what data to collect, how to collect it, and what to do with it. Ask yourself: “Is the data I’m collecting essential to my goals of increasing revenue and retaining customers? And will using it make those efforts easier?” If not, don’t collect it.
By tapping into a range of information, digital marketers can work out vital statistics like customer location, interests and even their day-to-day routines. These insights can help marketers deliver specifically tailored messages, and turn potential interest into sales.
It’s essential that the data you have on customers is correct, up-to-date and accessible. The cost of insufficient or bad-quality data can be high; studies by Experian and Gartner show that bad or erroneous data bring reductions in potential revenue of up to 25%.
Data quality tools form the basis of larger master data management (MDM) efforts to scrub poor quality data and get it in order. MDM tools can then create a central data hub, containing “single customer views” – a comprehensive portfolio of each customer, including history, profiles, and interactions with sales and support staff.
Exploiting Big Data
Before customer data is collected, analysed, or acted upon, a carefully thought-out plan must be in place.
First, identify your business need or the specific problem you want to solve. Within the organisation, there should be a focus on measuring, managing and analysing your marketing performance, and identifying ways to improve on what’s already being done.
Various marketing channels may play different roles in the customer buying cycle, and in customer acquisition. By conducting a Web analytics study, customer surveys, and other data analyses, you can generate a single customer view of your business, and determine how to reconfigure the marketing mix.
Various data sources such as customer relationship management (CRM) can reveal levels of customer engagement, and help you deliver business-specific loyalty programmes developed using advanced data studies of the business.
Doing Big Data analytics isn’t only about IT tools or technology.
Big Data means mastering context, developing the capacity to transform your data into insight, and changing your organisational culture if necessary. Business leaders, technology experts, and marketers will have to work together and gain a mutual understanding.
Businesses that start relying on Big Data for better insight need to prepare for the unknown financial consequences. There are privacy issues in gathering customer data, and the fines and lawsuits against companies that “misuse” data are just beginning.
True, the information is there. The question to ask is: “Just because we can use it to do X, should we?”
Break down your list of subscribers into groups by demographics, lifestyle, area of interest and degree of engagement, in order to craft relevant messages based on these factors.
Capture subscribers’ names, interests and email/website engagement. With this insight, you can address customers by name, reference their last interaction with your site, and make your messages speak directly to their specific areas of interest.
The technology is there, so use it. Exploit marketing software to send personalised messages in real time while a prospect is engaging with your brand. Don’t wait until they’re walking away.