Robots imitating humans, known from blockbusters like “Her” or “Ex Machina”, are slipping from cinematic fiction into reality. Although robots as actual members of our society are still part of distant future, intelligent algorithms capable of drawing conclusions and recommending solutions are already here. They can set up a marketing campaign automatically, prepare a report and recommend changes. Is there a place for a humble marketer?
How quickly do machines learn?
The amount of data about our customers is growing rapidly, too. What they click, what they like, what sites they visit, etc. No marketer is able to analyze this dataset by themselves. However, they can be supported by a system that analyzes data on its own and, what’s more, decide what to do next, and put the recommendations into practice.
In simple words, machine learning is a science dealing with artificial intelligence, and its purpose is to allow the possibility of automatic knowledge enrichment, reasoning and decision making by modern software. Machine learning allows us to put together all data about a customer and their behavior and obtain relevant solutions for business development and sales. Sounds like black magic? For the marketer, it is important to know what kind of data to expect and how to analyze them.
How to collect data for customer analysis
There are two ways to gain data for the analysis purposes:
- behavioral data – related to user behavior on the site – in which places on the website someone clicks, what is watching, what is buying. We receive them with pre-installed code tracking on the website,
- declarative data – acquired from filled forms, surveys, mailing databases, loyalty programs – data declared by the user. These are mainly information such as e-mail, name, address, age, education, etc.
The system not only collects all the data for us, he also processes it. However, marketer should know what to do with them later.
How to analyze data properly
1. From the general to the particular
Do not focus on the analysis of individual cases. Depending on the search based on the analysis of the entire data set.
2. The strength of correlation
Correlate the collection of data about your customers with other collections and search for dependencies. Check if the exchange rate, month, day, world events, etc. affect significantly change the buying behavior of customers.
3. Search for links
Pay attention to the fact that products are linked to each other – a person who is buying a laptop may be interested in a laptop bag, but probably will not buy the keyboard. For more information on related products and analysis of customer shopping cart, see our previous post how to analyze clients market baskets to increase sales.
4. The devil lies in the details
Consider that sometimes the seemingly unimportant data gives you some valuable insights about the customer.
- Operating system – informs us what kind of income can a customer have,
- Browser – it is worth to pay attention which browser is used by the majority of customers and so adjust pop-ups, dynamic blocks, etc. to properly show up in these systems,
- Devices – people who use more devices are more dependent on new technologies and tend to spend more time on the Internet – it is easier to hit them with on-line advertising.
5. Learn all about those who buy
Analyze data about customers who have purchased your products/services recently. Pay attention to what devices they used (or perhaps made a purchase in a physical store), what time, on what day of the week. Once you know the answers to these questions, it will be easier for you to select the appropriate promotion model. For example – if most people buy your products using a computer, don’t invest too much in mobile advertising. If many of them buy on Saturdays and Sundays, focus on promotion on these days.
6. Analyze the entire path purchase
Check the ways in which customers come to the purchasing process. Find out how many of them came through a campaign, who had previously visited your site or how many times they viewed a particular product. Monitor if they already were at a physical store, how much time they spent there. All this data may allow you to find patterns and figure out what influences their purchasing decisions.
7. Build segments
Automatic segmentation allows you to better analyze data and see which results in the increase or decrease of your sales. For example, if you notice a sharp drop in sales, while a large number of users were on the site, you should analyze it on the basis of separate customer segments. If the system creates them automatically, you can quickly locate the segment that contributed to the decline. Maybe the loss is generated by users 50+ (and perhaps you should improve the UX of the page and font size?), or people from specific regions, where the product was unavailable. Segments can answer many questions.
8. Do not judge by conversion
Conversion is often used by marketers in their reports. However, it should be noted that the conversion of 20% will achieve when 10 users visit the site and 2 makes a purchase, but also when 10 000 visitors will be on the site and 2 000 users will buy the service. Can you see the difference? Therefore is important to take into account in your reports nominal data (the number of page visits and number of transactions)
9. Pay attention to seasonality
It concerns industries selling seasonal products e.g. skis, skates, Christmas trees, bathing suits, etc. Smaller sales of bikinis in winter does not mean that the store is unprofitable. Likewise—if you notice an increase in access to the site in March, which does not go into sales, it doesn’t mean that we have a poor deal. The users must be just beginning to look for swimsuits before the summer season, but they need more time to decide. You should then take care of page optimization, launch new promotions, analyze how many of those visits ended up buying from a store etc. Real conversions should be count not only by the average value for the whole year but also for a particular season of the year.
10. Search for errors
Proper analysis of data allows you not only to find the accuracy and better personalize promotional activities but also quickly detect anomalies. This was the case of Walmart, which has long been monitoring all the data from sales of their products. On Halloween, they monitored the sale of special Christmas cookies. According to statistics, in many shops total sale of these products were high, but in some of them, it was zero. Analysts immediately contacted these stores to ask whether cookies are present in the right way – it turned out that in some shops they have not been laid on the shelves.
An example can be also statistics about the duration of page visit. If Internet Explorer users are staying on the website much shorter than the others, the system determines that they might not be interested in the offer. We as analysts can check the speed of page loading – this may be due to the fact that older browsers will be slower – adjusting the page to every browser, we can gain (if such users were plenty) new customers. The right analysis allows us to quickly find anomalies, and even faster to counteract them. It’s important to know these hot spots to identify and check why some results – from specific stores, communication channels, days – are so markedly different from the others.
11. Watch out for correlations
Much data correlated with each other, gives the illusion that both variables actually have an impact on each other. And here we see the advantage of a specialist who can look at these figures realistically and say whether that influence is not just a coincidence (the system currently can’t tell us that).
A few years ago, New York Times reported that according to studies there is a correlation between the murders in the United States and the market shares of Internet Explorer. But even if you can see a certain mathematical relationship between the abstract data, it does not mean that in fact there is an actual relationship between them. As you can see, the presence of a person in the process of data analysis is still necessary.
A good analyst is worth their weight in gold
Machines have permanently changed the responsibilities and requirements of different jobs. Many of them will be fully or partially automated and replaced by intelligent systems. But that doesn’t mean people will no longer be needed. Campaign creation, management and promotion through developing artificial intelligence will never be the same again. We can observe that the amount of data and information about clients is still growing. The question is not whether you should analyze clients’ data, but how to do it. A complex analytical system would be useful in this process, but also a person who will be able to draw valuable conclusions from this data, which can help in creating a more effective strategy and provide growth in profits and sales.