New case studies: Decathlon and MyToys

 

neue-case-studies

We are happy to announce the publication of two new case studies about the work with our clients Decathlon and MyToys. The case studies show you two best practice examples how effective personalized campaigns help you to gain additional online shop revenue.

You can download the case studies here:

We thank Decathlon and MyToys for the great cooperation and we are looking forward to a lot of successful campaigns. If you are interested in setting up personalized campaigns for your own online shop, contact us here.

New customers: Channel 21, Deerberg and La Grande Récré

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We are happy to welcome Channel 21, La Grande Récré and Deerberg among our new clients of 2016.

  • Channel 21 is a German onlineshop for beauty, fashion and health products. Similar to its homeshopping TV channel of the same name, customers can find a wide range of exclusive product offers on the onlineshop.
  • With Deerberg we have won a new partner in the fashion sector. The company is specialized in selling fair-produced, organic clothes instore and online.
  • La Grande Récré is a French retailer specialized in kids toys. In addition to its 200 stores in France the company provides a huge variety of products for children online.  

We are looking forward to effective campaigns and help them generate a lot additional revenue with effective, personalized campaigns.

Cart Abandonment Can Cost You More Than 50% Of Your Online Shop Revenue

Shopping Handbags
Online shoppers often add items to the basket without buying them.

Online shoppers generally add items to the shopping cart within a short time after having arrived on the shop website. Nonetheless many of them leave the shop without buying. Among some of our clients more than 50 % of the visitors start their visit with a full basket. They have obviously stopped the purchase before. This is a remarkably high amount and much revenue lost if they never complete their purchase.

Other online shops experience an even worse situation. The Research Institute Baymard has collected shopping cart abandonment case studies since 2006. The result: The average cart abandonment rate is close to 70 %. The interesting questions are: Why do online shoppers abandon carts? And what can I do as a retailer to prevent it?

The 10 Most Common Reasons For Shopping Cart

A Many reasons for cart abandoned have been identified. A list of the top reasons was recently published as part of the UPS Pulse of the Online Shopper study. In cooperation with the internet analytics company comScore UPS interviewed 5,000 online shoppers and discovered the 10 most common reasons for abandoned carts:

  1. Shipping costs made the total purchase costs more than expected (56%)
  2. My order value wasn’t large enough to qualify for free shipping (45%)
  3. I was not ready to purchase, but wanted to get an idea of the total cost with delivery for comparison (44%)
  4. I was not ready to purchase, but wanted to save the cart for later (43%)
  5. The item was out of stock (42%)
  6. Shipping and handling costs were listed too late during the checkout process (34%)
  7. I needed the product within a certain time frame and the shipping options offered didn’t meet my requirements (28%)
  8. I didn’t want to register/create an account just to make a purchase (27%)
  9. The estimated shipping time was too long for the amount I was willing to pay (26%)
  10. My preferred payment (i.e. bank transfer, debit card, PayPal, Google Checkout) was not offered. (24%)

Solutions For Shopping Cart Abandonment

Let’s have a closer look at the results and what you can do about it. We find four main categories to explain abandonment. The good news is, three categories can be easily dealt with by optimizing your ordering process:  

  • Check-out

The first optimization area is the check-out process. The surveyed customers complained about hidden costs. Create a transparent checkout process and list all accruing charges directly at the beginning. The online shoppers shouldn’t get annoyed by unpleasant surprises at the end of the purchase process.

Another pain point was the obligation to create an account. Offer the possibility to buy your products without an account and make the purchase process as comfortable as possible.

  • Delivery

Online shoppers frequently mentioned the importance of a quick and comfortable shipping process. Guarantee the direct availability of your products and offer the most common shipping options.

Make sure to communicate the guaranteed shipping date and available options prominently on your site. Your potential customers have to find this information at a glance.

  • Pricing

Pricing is an important factor, when it comes to the final check-out. Especially, high shipping costs keep a lot of online shoppers from buying. Offer free shipping or keep the shipping costs as low as possible.

Card Abandonment in the areas of check-out, delivery and pricing can be tackled relatively easy. But there is still one category left, that needs more consideration.

  • Buying Intention

A lot of the interviewed online shoppers mentioned, that they haven’t been ready to buy. They just browsed the shop to look for more information about the product or the shipping. This means these online shoppers are an enormous chance to boost your sales. In general, they are interested in your product and services but they do need a very personal interaction to make sure that they will turn into customers.

Identify And Persuade Relevant Online Shopper

All you need to do is convince them that you have the products for their needs. Easier said than done. How can you identify the promising customer group? And what do you have to do to persuade them? In general, there are two ways to attract them.

The first one is to contact them after they have already left the online shop. A lot of companies try to address their online shop visitors via retargeting. They mark every visitor with a special cookie and show them online ads on other website or on social media. Another solutions is to address them by e-mail. If your visitors have created an account or signed up for an newsletter, it is possible to address them with personalized mailings afterwards.

Personalized campaigns can help you to persuade undecided online shoppers.
Personalized campaigns can help you to persuade undecided online shoppers.

The second solutions is to address online shoppers directly on the online shop while they are seeking for relevant information. It is possible to distinguish between buyers and non-buyers by using an algorithm, called Random Forest. In this way, you can show personalized campaigns only to users, who would not have bought otherwise.

For example, the interviewed people mentioned, they had been looking for the total costs and shipping fees. Why not offering them a voucher for free shipping or a product discount? A lot people mentioned that they just want to learn about the products but necessarily purchase them at your particular shop. Why not highlight additional benefits of purchasing at your shop or offering them free give-a-ways, if they order the product directly?

To sum it up: shopping cart abandonment is a huge problem for online shops, but you can do something about it. It’s your turn. Start optimizing your online shop today and boost your revenue. The following checklist, will help you reach your goal.

Checklist: 6 tips to avoid shopping cart abandonment
Checklist: 6 tips to avoid shopping cart abandonment

If you need help with running personalized campaigns on your online shop, get in contact now. Akanoo engages online-shop visitors with effective campaigns, while they are still surfing the shop site. Unhappy visitors and abandoned carts are avoided and revenues increased.

How To Predict Purchase Probability With Random Forest Models

Predicting_Purchase_Probability_With_Random_Forest

Will the user abandon my shop without purchasing? This is one of the most common questions every shop manager is facing. A lot companies are collecting and analysing huge amounts of user data (click behavior, previous purchases etc.) to answer it. Did you ever wonder how you can predict purchasing probability while the visitor is still surfing the shop site? First you need to collect the right user data. Finally smart algorithms like Random Forests provide you with valid predictions who will buy or not.

Steps For Predicting Purchase Probability
Steps For Predicting Purchase Probability

Multi-staged Questions Help To Identify Buyers

It is necessary to know about Decision Trees to understand Random Forest algorithms. A Decision Tree is a group of questions you ask to get to a conclusion step-by-step. In case of our purchasing probability example you could start with “Do we know the customer?” followed by further questions like “Has the user viewed more than 3 products?”, “Does the visit last longer than 5 minutes?”, “Are already products in the basket?” and many more. In the end you classify the visitor as a buyer or a non-buyer for every single branch of the tree. The graphic below shows the principle of a Decision Tree analysis. Of course, in reality the analysis will depend on a variety of additional factors. For example, Akanoo uses combinations of over 50 independent variables to calculate purchasing probabilities.

Example_Decision_Tree
Simplified Graphic For A Decision Tree Analysis

How Can I Use Random Forest To Increase My Online Shop Revenue?

Although it looks like a great tool to predict probabilities, there is one essential problem: Overfitting. Questions for a Decision Tree are created with the help of a training data set. For this training data set the predictions are very reliable. But if you are trying to generalize and adapt it to new data sets, the predictions aren’t that good anymore. The Decision Tree is adapted too much to the initial training data set – it is overfitting.

To avoid it, you can use a combination of different trees – a Random Forest – and built an average value. Usually a Random Forest consists of about hundreds of different Decision Trees and supplies more precise, applicable results to new data.

Apart from predicting the purchase probability, it is useful for a wide range of E-Commerce questions, e.g. regarding:

  • Sales increase: Will the visitor add a second product to the shopping cart?
  • Sales decrease: Will the visitor remove a product from the shopping cart?

Finding answers to questions like this will be very helpful for optimizing your online shop. For example, Akanoo uses the prediction results to show users personalized campaigns highlighting top sellers, giving individual coupons or guiding the visitor to more relevant selections of products to increase revenues and profits of online shops.

If you have any questions, how your online shop can profit from using Random Forest and personalized incentives, send us a message. We are happy to help you.

What Is Data Mining?

child with binoculars

What is Data Mining? And what is Predictive Behavioral Targeting again? With all the technical terms in the field of onsite user analysis you can easily lose track of what’s going on. That’s why, we tried to define the terms for you and set the record straight.

How can you define Data Mining?

Data Mining is a generic term for different types of data analysis. Data Mining experts discover hidden relationships in large amounts of data that are useful for companies or research institutions. The main difference in comparison to other data analysis techniques is that Data Mining doesn’t presuppose an assumption beforehand. The assumptions will be developed during the analysis process, known as KDD (Knowledge Discovery in Databases). Data Mining is only a small part of the whole analysis process.

Data Mining in eCommerce

Why is Data Mining important for online shops?

Data Mining is often applied to shopping cart analysis in eCommerce. Retail companies are using a special Data Mining technique called Pattern Mining to find relations within the customer’s shopping habits like: 50% of our customers, who bought a scarf, also bought gloves. These recurring sequences are called frequent patterns and are used for product recommendations („Customers Who Bought This Item Also Bought“).

If you combine the Pattern Mining technique with statistical data like surveys or demographic data and use your findings to predict future behavior, you call this Predictive Behavioral Targeting. Akanoo applies Predictive Behavioral Targeting to avoid abandoned carts. We are looking for behavior sequences that are occurring again and again. This could be a specific order of product sites or patterns in date and time of the visits. As soon as we have discovered frequent patterns, we use them in combination with available demographic data to predict the probability of intentions such as “will not convert”, “will not add second product to basket”, etc.

To cut a long story short: Data Mining is an important data analysis technique to extract valuable, hidden information from large data sets. It is particularly help in digital marketing and assists transaction-based websites in maximizing their profits.

Do you like to maximize your profit with Predictive Behavioral Targeting? Contact us now!

Three effective tools for personalizing your transaction-based website

No question: on-site personalization has been a burning issue in online-marketing and e-commerce  for quite some time. But what are the nuts and bolts to successfully personalize your website? We took a closer look at recent studies to find out what consumers really want in terms of personal interaction on a website. The info graphic below sums up our findings.

graphic_personalization_online_shoppers
This infographic sums up different studies about the personalization preferences of online shoppers.

For instance, the online publishing network Retail Touchpoints (on behalf of Magnetic) has surveyed 200 consumers. The result: personalized product recommendations are important. Primarily, users request to be informed about:

  • personalized offers that are limited for a certain time,
  • individually relevant products with very good ratings,
  • products based on their previous purchases.

The survey of AgilOne draws a similar conclusion. The 3,000 interviewees are preferring personalized offers and product recommendations besides an automatic refill function.

The consulting company Accenture has also asked consumers about their personalization preferences. They have surveyed about 1,000 Americans. The result: they like the following functions above all:

  • Exclusive offers
  • One-Click Checkout
  • Reminder for products that they might need soon

What conclusions can we draw for website personalization?

Although the studies have surveyed people from different countries and don’t provide representative results, they indicate that personalized product recommendations, an automated ordering process & personalized offers/coupons are among the most important features for online shoppers. We will now introduce all of them in detail and give you an idea how to implement them properly.

1. Personalized product recommendations

Product recommendations help visitors to discover relevant products that fit their current interest. Probably, the best known provider of  personalized product recommendations is Amazon. Everybody has come across its product recommendations like “Customers Who Bought This Item Also Bought”.

Screenshot Amazon recommendations
Amazon provides good examples for personalized product recommendations.

But not only big players like Amazon can use personalized product recommendations. Smaller retailers can easily implement recommendations today, too. There are several software vendors, such as Nosto, Econda, Prudsys or Epoq, that are provide powerful solutions for small and medium-sized enterprises.

2. Automated ordering processes

Another highly-regarded feature are automated ordering processes. They draw from registered  visitor information to speed up the checkout process and make it much easier.  The most effective way to automate the ordering process is to offer one-click checkouts, such as the one from Daniel Wellington pictured below.

one klick checkout daniel wellington
The Daniel Wellington shop provides a good example for one-page-checkouts.

The advantage: users do not have to click through several pages before they can buy a product. They enter all information in one page and check everything easily. In combination with an user account the checkout process becomes effortless and a matter of few seconds before a transaction is completed. Previously saved the preferred method of payment and shipping address could be selected by the system in advance.

3. Personalized offers and coupons

Another feature mentioned by the interviewees again and again are personalized offers and coupons. Similar to the personalized product recommendations, novel services such as Akanoo analyse user and decide in real time to provide personalized offers to  online shoppers. This could be a promotional codes or incentives like in the example of the GAASTRA online shop below.

Each online shop is unique

The three approaches to personalization above show promising performance across industries. Yet every online shop and transaction-driven website is unique. The approaches likely improve performance, but they don’t have to.  Every online shop has specific target groups with distinct needs and tastes. It is extremely important to test the different personalization options and optimize them continuously.

If you need assistance in the automated optimization of personalized incentives, Akanoo can offer you the optimal solution. Contact us today. We are happy to help.