Marketing executives have an insatiable desire to get to know more about their potential customers. Big data analysis may be the answer to their requirements.
In theory, every aspect of our lives can be quantified and measured. Big data deals with how a company can gather and analyse such information. Data storage capabilities are roughly doubling every 40 months – this means that any company today can capture any kind of data from innumerable sensors and input devices, and store it all with relatively low costs involved.
Problems arise when such data needs to be analysed and some sort of context extracted to predict trends, target potential customers and help in decision making. Most standard software packages such as relational database management systems, which have been used for over 30 years, are unable to handle such vast amounts of data.
Just a few years ago data collection from a marketing perspective involved analysing newsletter subscribers, tracking repeat customers and looking at their purchasing history. However nowadays marketers can access data such as social media use, exercising habits, caloric intake, time spent in the car, web browsing history and practically any other variable imaginable.
Big data analysis also allows marketers to use lead scoring. This involves assigning points to each potential customer according to trends and data gathered. For instance, points may be assigned to customers who bought similar services recently or according to income levels. Companies may decide later to focus most of their energy on the top scoring customers and direct sales calls at them, while they may send out inexpensive e-mail snapshots to others who scored on the lower strata.
Predictive lead scoring is later used to assess how successful and profitable such campaigns were. Variable weights are adjusted, so in the future customers profiled who were previously targeted and did not make a purchase are not included in the upper strata and vice versa. This means that over time the system becomes more intelligent and practically starts learning from its own mistakes, at the same time allowing decision makers to allocate marketing budgets and resources using a previously undreamt of level of accuracy resulting in an ever increasing return on investment.
This level of data analysis can lead to increasing levels of customer retention and loyalty
This level of data analysis can lead to increasing levels of customer retention and loyalty. In this area big data is able to help marketers uncover what makes customers return, answer questions such as why certain customers prefer a certain brand over another, and what ultimately influences their buying decisions.
Expenses and return on investment can nowadays be better quantified. In the past marketers who ran an advertising campaign on a television network were unable to tell exactly how many customers bought their product as a direct result of seeing the advert or whether they just chose it at random when browsing through the supermarket shelves. At the most basic level of tracking, today any company is capable of knowing if anyone made a purchasing decision after following an online advert, e-mail shot or via a search engine. This means that marketers may adjust budgets according to which strategy proves to be most profitable.
Big data analysis is not all roses though and marketers are faced with different challenges, most obvious of which is to decide what variables and data to actually gather and analyse. In some cases more data does not necessarily mean better results. Results come from the right data and collection methods.
That brings us to another headache – how can marketers actually analyse everything?
When a company starts considering the introduction of big data collection and concepts, the enterprise might seem a bit overwhelming at first. Experts advise that in order for such a culture change to be successful, a company has to tackle one objective at a time rather than trying to completely overhaul the way it gathers information about its customers and analysing it. So at first a company might focus on installing better sensors at its outlets, record and track purchase habits and only later try to use this information to extract patterns and correlations which can assist decision takers in identifying potential customers and therefore help identify more business opportunities.
On the other hand businesses have to pay attention not to fall in the big data paradigm trap and expect data collection and analysis to solve all of their problems. Big data brings with it several legal risks as well, most obvious of which are privacy and data protection infractions and the potential bad publicity that ensues if such infractions are made public. Also, decision takers might misinterpret results and confidently invest heavily in marketing campaigns that eventually yield no results.
Big data analysis software isn’t easy or flexibile to use. This means that finding and hiring the right person for the job might be tomorrow’s challenge for any marketing executives.
One final predicament might be IT security. In today’s world of hackers and data thieves, big data is a shining treasure trove for such individuals and for the competition. We frequently hear of data breaches using simple tools, so as more data is gathered, the potential for a leak or hack increases drastically. Most companies who suffered a hack go through a public relations nightmare in the months after the issue becomes public knowledge.
Executives must learn to balance their efforts and avoid the urge to become completely data-processing oriented and remember the basic marketing forces of customer value and after sales service. In spite of everything we all still are more than just ones and zeros.
Ian Vella blogs at www.ianvella.com.
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