How End-to-End Analytics Helps Businesses

OWOX BI End-to-End Analytics

End-to-end analytics is not just beautiful reports and graphics. The ability to track the path of each client, from the first touchpoint to regular purchases, can help businesses reduce the cost of ineffective and overvalued advertising channels, increase ROI, and assess how their online presence affects offline sales. OWOX BI analysts have collected five case studies demonstrating that high-quality analytics helps businesses be successful and profitable.

Using End-to-End Analytics to Evaluate Online Contributions

The situation. ​A company has opened an online store and several physical retail stores. Customers can buy goods directly on the company’s website or check them out online and come to a physical store to purchase. The owner has compared revenue from online and offline sales and has concluded that a physical store brings much more profit.

The goal​. Decide whether to back away from online sales and focus on physical stores.

The practical solution. ​The lingerie company ​Darjeeling ​studied the ROPO effect — the impact of its online presence on its offline sales. Darjeeling experts concluded that 40% of customers visited the site before buying in a store. Consequently, without the online store, almost half of their purchases wouldn’t happen.

To get this information, the company relied on two systems for collecting, storing, and processing data:

  • Google Analytics for information about users’ actions on the website
  • The company’s CRM for cost and order completion data

Darjeeling marketers combined data from these systems, which had different structures and logic. To create a unified report, Darjeeling used BI system for end-to-end analytics.

Using End-to-End Analytics to Increase Return on Investment

The situation. ​A business uses several advertising channels to attract customers, including search, contextual advertising, social networks, and television. They all differ in terms of their cost and effectiveness.

The goal. ​Avoid ineffective and expensive advertising and use only effective and cheap advertising. This can be done using end-to-end analytics to compare the cost of each channel with the value it brings.

The practical solution. In the ​Doctor Ryadom chain of medical clinics, patients can interact with doctors through various channels: on the website, by phone, or at the reception. Regular web analytics tools weren’t enough to determine where each visitor came from, however, since data was collected in different systems and wasn’t related. The chain’s analysts had to merge the following data into one system:

  • Data about user behavior from Google Analytics
  • Call data from call tracking systems
  • Data on expenses from all advertising sources
  • Data about patients, admissions, and revenue from the clinic’s internal system

The reports based on this collective data ​showed which channels didn’t pay off. This helped the clinic chain optimize their ad spending. For example, in contextual advertising, marketers left only campaigns with better semantics and increased the budget for geoservices. As a result, Doctor Ryadom increased the ROI of individual channels by 2.5 times and cut advertising costs in half.

Using End-to-End Analytics to Find Areas o​f Growth

The situation. ​Before you improve something, you need to find out what exactly doesn’t work correctly. For example, perhaps the number of campaigns and search phrases in contextual advertising has increased so rapidly that it’s no longer possible to manually manage them. So you decide to automate bid management. To do this, you need to understand the effectiveness of each of several thousand search phrases. After all, with an incorrect assessment, you can either merge your budget for nothing or attract fewer potential customers.

The goal. Evaluate the performance of each keyword for thousands of search queries. Eliminate wasteful spending and low acquisition due to incorrect assessment.

The practical solution. To automate bid management, ​Hoff​, a hypermarket retailer of furniture and household items, connected all user sessions. This helped them track phone calls, store visits, and every contact with the site from any device.

After merging all this data and setting up end-to-end analytics, the company’s employees began to implement attribution — the value distribution. By default, Google Analytics uses the last indirect click attribution model. But this ignores direct visits, and the last channel and session in the interaction chain receives the full value of the conversion.

To get accurate data, Hoff experts set up funnel-based attribution. The conversion value in it is distributed between all channels that take part in each step of the funnel. When studying the merged data, they evaluated the profit of each keyword and saw which were ineffective and which brought more orders.

Hoff analysts set this information to be updated daily and transferred to the automated bid management system. Bids are then adjusted so that their size is directly proportional to the ROI of the keyword. As a result, Hoff increased its ROI for contextual advertising by 17% and doubled the number of effective keywords.

Using End-to-End Analytics to Personalize Communication

The situation. In any business, it’s important to build relationships with customers to make relevant offers and track changes in brand loyalty. Of course, when there are thousands of customers, it’s impossible to make personalized offers to each of them. But you can divide them into several segments and build communication with each of these segments.

The goal. Divide all customers into several segments and build communication with each of these segments.

Practical solution. Butik​, ​a Moscow mall with an online store for clothes, footwear, and accessories, improved their work with customers. To increase customer loyalty and lifetime value, Butik marketers personalized communication through a call center, email, and SMS messages.

Customers were divided into segments based on their buying activity. The result of it was scattered data because customers can buy online, order online and pick up products in a physical store, or not use the site at all. Due to this, part of the data was collected and stored in Google Analytics and the other part​ ​in the CRM system.

Then Butik marketers identified each customer and all their purchases. Based on this information, they determined suitable segments: new buyers, customers who purchase once a quarter or once a year, regular customers, etc. In total, they identified six segments and formed rules for automatically transitioning from one segment to another. This allowed Butik marketers to build personalized communication with each customer segment and show them different advertising messages.

Using End-to-End Analytics to Determine Fraud in Cost-Per-Action (CPA) Advertising

The situation. ​A company uses the cost-per-action model for online advertising. It places ads and pays platforms only if visitors perform a targeted action such as visit their website, register, or buy a product. But partners who place ads don’t always work honestly; there are fraudsters among them. Most often, these fraudsters substitute the traffic source in such a way that it seems as if their network has led to the conversion. Without special analytics allowing you to track every step in the sales chain and see which sources influence the result, it’s almost impossible to detect such fraud.

Raiffeisen Bank was having issues with marketing fraud. Their marketers had noticed that affiliate traffic costs had increased while revenue remained the same, so they decided to carefully check the work of partners.

The goal. Detect fraud using end-to-end analytics. Track every step in the sales chain and understand which sources influence the targeted customer action.

Practical solution. To check the work of their partners, marketers at Raiffeisen Bank ​collected raw data of user actions on the site: complete, unprocessed, and unanalyzed information. Among all clients with the latest affiliate channel, they chose those who had unusually short breaks between sessions. They found that during these breaks, the traffic source was switched.

As a result, Raiffeisen analysts found several partners who were appropriating foreign traffic and reselling it to the bank. So they stopped cooperating with these partners and stopped wasting their budget.

End-to-End Analytics

We’ve highlighted the most common marketing challenges that an end-to-end analytics system can solve. In practice, with the help of integrated data on user actions both on a website and offline, information from advertising systems, and call tracking data, you can find answers to many questions concerning how to improve your business.

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