Several years ago, one of the trendiest technologies used for collecting customer information was introduced. It is now referred to as big data. What is big data? Simply put, big data can be described as the process of using a large amount of data collected from customers and clients to study and analyze different patterns of behavior. Companies can then use this information to predict customer behavior and increase sales.
Many data analysts believed that all they needed to do was use big data to look for correlations and make predictions based on their findings. As it is well known, correlation is when one variable is linked to another. However, if two variables correlate, that doesn’t always mean that one caused the other. (How does the old saying go? Correlation does not equal causation?)
For example, between 2000 and 2009, there was an increase in the number of divorces in the State of Maine. At the same time, consumption of margarine decreased during that time. But, margarine and divorce have nothing to do with each other. The challenge of using big data is discovering which correlations mean something for business and which correlations are not relevant.
The alternative to big data
Harvesting mass amounts of data may work for some companies, but for medium size and especially small businesses this may not be the best marketing strategy. Here are some examples of what can be a source of data for which methods of working with large data amounts are needed: GPS signals from cars to the transport company, the amount of bank transactions per day, number of purchases in a large retail network, etc.
If you’re not using big data to get information on your consumers, what other techniques can your company use? Soon after big data conquered the marketing agenda in thousands of companies, the alternative concept of lean marketing was introduced.
Lean marketing puts emphasis on short project cycles. Short cycles allow you to adapt quickly to changing marketing conditions. It can take months to make an important marketing decision based on big data analysis due to the massive amounts of information that must be analyzed. It’s all about having the swiftest reaction possible to emerging trends on the market.
Lean marketing vs. Big data
Lean marketing is a search for new data and approaches, it is focused on experiments and growth. But in lean it’s important to test the hypotheses every time and draw conclusions from concrete results. An example from “Lean Marketing for Startups: Agile Product Development, Business Model Design, Web Analytics, and Other Keys to Rapid Growth” by Sean Ellis (available here):
You can use an event for gathering customer feedback. You can make changes during the event based on how people respond to your displays and pitch.
Lean marketing is about being agile, about viewing each campaign or marketing activity as one step in the ever-improving progress towards customer acquisition and ultimate customer satisfaction. An example from “A Lean Marketing Revolution: The Timeless-Know How Principles” by John Errigo (available here):
Instead of spending days brainstorming and writing ten new articles for your company blog, you can list a few topics you want to cover, and send the list to your readers via email or social media. Ask them to choose which ones they want to read about first. This helps you make the right choice for topics before you spend time on writing.
There is no universal hypothesis, in contrast to traditional marketing. Each client is a separate case. Lean marketing helps to generate a lot of hypotheses quickly, roll out the MVP and get feedback from the audience. Due to the iterative work cycles, useless ideas are quickly eliminated, the money and resources are not spent in vain. Time is saved. The team is constantly learning from the continuous incoming feedback.
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