Previously, only the most advanced marketing departments worked with analytical data. Now even a small business begins to build and adjust its strategies based on the data. Those who still do not do this, begin to lose in the struggle for the client.

More and more marketers are faced with problems when working with Big data

Opportunities analysts are improving every year, and at the same time, it is becoming easier to use. Any business, regardless of size, as well as marketers with little or no experience, now have the opportunity to access efficient services and analytical tools.

Despite the fact that this opens up innumerable opportunities for the business, this situation raises the risk of error when it comes to marketers who rely solely on analytical data.

There is a stereotype that the more data - the more effective your solutions will be, but in practice this may not work. Marketers often face pitfalls when working with "big data": I'll tell you about these hidden threats.

Misconceptions about marketing Big data

Working with Big data, you must first know about the most common misconceptions, which in the end can lead to disastrous decisions.

1. Raw initial data is trustworthy by default

Of course, dry data by itself is always correct, but it is worth remembering five factors for assessing the quality of information:

Integrity and completeness
Consistency (consistency)
Accuracy
Correctness
Relevance
When the quality of the data is inadequate for at least one of the criteria, the company can make a serious mistake by incorrectly building a strategy or investing funds in unpromising projects or ideas.

2. All information should be obtained from one source

In fact, this happens rarely. Most often, the data set comes from independent sources and passes through several "hands" before getting into the marketing department.

In this case, often each of the data sources has its own system of "cleaning" them, and some of these systems may not work very well. In such a chain of information transfer, there are almost always inevitable contradictions and problems with consolidation.

3. The use of analytical data is free from the human factor

We are increasingly confronted with incorrect analysis of data, and this is due to the development of technology. Today, any marketer has access to analytics services. When a person with little experience in data analysis starts using such services, he often makes erroneous conclusions, misinterpreting the information and paying much attention to minor aspects.

In addition, the marketer can firmly believe in the reliability of the analysts and, as a consequence, act contrary to common sense - which can be costly for the company.

Rules for marketers when working with Big data

Many marketers misconceptions about the analyst. Therefore, it is important for any specialist to learn how to collect and understand data.

Their analysis still gives a serious competitive advantage and allows you to quickly process huge amounts of information - so marketers must continue to use large data in their work. But they also need to carefully evaluate the results of data analysis and identify those insights that can bring real benefits to the business.

Let's consider three key rules, the adherence to which will allow the marketer to make responsible and adequate decisions on the basis of analytics.

1. Check the "cleanliness" of the data

Do not start working with the data, without first checking for compliance with the five factors for assessing the quality of information (see above). Avoid any incomplete or inconsistent data - in the future, their low quality will make your work more difficult.

Automating the collection of information will help to get more correct and relevant data.

2. Bring your entire "chain" of data to a single standard

If information comes from different sources, then the emergence of contradictions between different pieces of data is almost inevitable. Accordingly, specialists should bring their relations with each of the participants of the "chain" to a unified standard.

Create a schedule that all participants will adhere to. Automate the information entry procedure to provide a single integration for all data providers. And, finally, deliver these standards to all participants.

As a result, you can create a process of working with large data, which reduces the risk of errors to a minimum.

3. Train employees in an adequate relation to large data and their competent interpretation

To be sure that your marketers can make informed and effective decisions, start with the search of initially experienced professionals. In the future, you need to make sure that all members of the team working with Big data understand the important nuances associated with collecting information and making decisions based on analytics.

It is especially important for employees to understand the shortcomings that are an integral part of working with data.

Marketers should learn that although the data provide a good starting point for decision-making, it is possible to achieve the most effective results only with the help of a combination of competent analytics and developed business intuition.

Case studies: the most efficient work of companies with Big data

Costco Wholesale American Self-Service Network Case

The two most noteworthy cases associated with large data and this company are related to the safety of food consumers. In 2014, the US has information on the large-scale infection of some fruits with listeria - a bacterium that, if ingested, can cause serious harm.

Costco needed just one day to analyze the collected data and, by demonstrating the exactest targeting, to notify only those consumers who bought unwanted (and, possibly, infected) products.

The buyers were called, and then they sent an email in addition. The retailer uses Big data to ensure the security of its customers on an ongoing basis - this is just one of the most revealing cases.

Earlier, in 2010, the company helped state organizations to identify the outbreak of salmonella outbreaks - also thanks to their huge arrays of large data and skillful work with them.

Red Roof Hotels Network Case

If your flight is canceled at the moment when you have already arrived at the airport, then most likely you will be most worried about the prospect of spending the next day (or night) in the waiting room. This is the "pain" used by marketers of the Red Roof hotel chain.

Most of the hotel network is located near the airports, and the analysis of large data allowed marketers to develop a campaign focused on those locations where weather conditions most often tear off flights - and where the largest number of potential customers who need an inexpensive hotel room nearby collect.

This approach increased network growth by 10% in the areas covered by the advertising campaign.

Big data is only part of the puzzle

Data analysis is at the forefront of innovation in digital marketing, and it seems that this trend will continue to develop rapidly in the coming years. But despite the fact that advanced analytics provides an excellent opportunity for marketers to better understand the audience and improve the user experience, large data is only part of the puzzle.

Wholly relying solely on analytical tools in the process of making business decisions, you can incorrectly plan your strategy and face serious consequences.

Business solutions should be based on a healthy balance between the data and the trivial intuition that comes with years and experience. If the company's marketers can adhere to this balance, they can make more informed decisions in the long term.