Lots of companies collect their email addresses database in order to keep customers or prospective buyers at hand. This is a really powerful tool that makes possible working with a loyal audience and increasing sales. Moreover, earlier we already wrote about tips that can improve the effectiveness of your website. So, if you have not yet started collecting emails, I would highly recommend to think it over after reading the article. This would help you to expand your business significantly.
Today I’ll tell you about a real case that gave an opportunity to boost quantity of orders automatically. It is mircen.kiev.ua project development on e-commerce field. This project has a tested idea that makes a profit. This platform gives an opportunity to compare prices for different goods. We put a special form on each page of the site in order to collect emails. Therefore, users could be quickly subscribed to updates or notifications on goods discounts. Developed script automatically sent an email after the subscription. Mailing is to be performed once per 1-2 months. Within this period many users could lose their interest in buying goods for which they have been subscribed. Having a huge range of goods our site could meet any buyer request. However, we realized that we were losing potential buyers and profits consequently.
After making a brainstorm and analyzing the audience, we have summed up that users are indented to change their decisions just before the purchase is to be done. They analyze prices, same goods main features and new arrivals. And after a certain period of time they finally make a purchase. Therefore, we have thought if we could get the customers brains and offer exactly the product they are looking for. It’s been quite a difficult task…
Still, we did our best and found an interesting solution, which helped us to get customers mind and made the most relevant offer. We decided to analyze every user behavior. Moderate script recorded all visited pages, filters and parameters for every request. We gathered all possible information. Thus, history of every user site browsing was collected. Our site had 250 000 unique users per month. It was a big scope of data entries. They were estimated at tens of millions. Then a question aroused on how to analyze all these data in order to give a perspective customer the most interesting offer.
It took several weeks to solve this problem. We wrote a script that taken into account all available parameters and sorted them out. We highlighted every feature that a user drew attention to as the main parameters for the offer development. We also had default parameters, which were worth for analyzing. For example, for mobile phones we took into account their screen size, the operating system, RAM, their price. Having collected all necessary parameters, the script searched for the corresponding models and notifications sent if the cost for some of them had been changed. If these parameters were considered as new, we sent a notification to users as to a new arrival. Only during a couple of days when the algorithm was working, we increased mailing conversion rate from 2% to 15%.
We identified each user by a unique code that we had written in cookies in order to keep accurate parameters. For mass mailing we used automatic and unique tags for each email. Thus, when the user clicked the link and went to the site, we had already known who exactly came to us. After conducting a work on scripts developing, we started sorting the product based on its history and preferences. The more user was available on the site, the more information about him we collected. And this made it possible to form unique proposals more accurately for each person. Doing this, we increased loyalty, average views and number of purchases, what is more important.
So we decided to move forward. We already had a ready to purchase audience which was tracked by our team. Having a discussion with the CEO of the company, we had a question arisen: is it possible to improve our result? Therefore, we decided to perform even deeper iteration and identified the most active users. Further, we sorted them into the following groups: -the most active, involved and slightly active. So that we had profound and detailed information about their behavior, such as which products were viewed, how long they were interested, whether they read reviews, etc. Basing on this statistics we noticed that after 5-6 weeks a user reduced its activity. During this very time he managed to choose and buy the appropriate product. In this case we decided to send a letter with a request as to providing us with a comment or give a review on goods in order not to lose connection with the client. This made possible to increase the number of useful reviews.
So let’s sum up the gained results:
Mass mailing conversion: 15%
Involvement: 6.3 pages per user
Activity time: 2.45 minutes
It was a quite complicated task, which gave us good results and increased number of sales.