How to solve the biggest e-commerce reporting challenges
There is more and more data online, and to grow faster than competitors, you need to use this data in full. In this article, we consider the main problems that the majority of marketers face while working with reports.
Over the past year, the pandemic has turned online shopping from an additional convenience to an urgent need. The sphere is rapidly developing, and with it, competition for buyers is growing. Among the main problems for e-commerce businesses today are:
- Adapting to digital commerce
- Customer retention
- Higher traffic costs
- Third-party cookie restrictions
- Keeping up with competitors
Users are already accustomed to their data being used by companies, but in exchange, they expect to get relevant information about products at any time and place. Moreover, the information presented should vary depending on past user behavior. Everything becomes even more complicated because a target audience’s behavior is different at different touchpoints and therefore requires a different approach at each. For this reason, marketers today use a vast number and variety of channels.
Consumers use search engines (52%), social media (43%), and customer reviews (37%) to research products. The purchase journey is becoming more fragmented as consumers use more different devices, with each audience using multiple channels/sources to find answers.
Correctly using all this audience data brings value to an entire business. Remember that if you want to make data-informed decisions, you must have a lot of quality data and use it correctly!
However, that’s where the problem lies. The overwhelming amount of data coming in and the limited time and resources to work with that data reduce the effectiveness of marketers’ work.
We’ve just mentioned that the more data, the better. But you probably know the other side of the coin: the more data from different sources, the more problems. Let’s take a look at what challenges you’ll encounter when working with reports.
Data collection is the process of gathering all the fragmented marketing data you need to build any reports and set up advanced analytics.
Why do you need it? Just as the theater begins with a hanger, reporting starts with data collection. This is the first and most crucial step. If you don’t collect all data or you collect the wrong data, you will take actions based on faulty information. And this won’t end well.
You have to collect data from all customer touchpoints to be sure that you factor in everything:
- Cost data from advertising services
- Data about user behavior on the website
- Call tracking, chatbot, and email data
- Actual sales data from your CRM/ERP systems
- Other data
In the end, nobody wants to go over the budget. Keep in mind that preventing an error in data collection is easier than correcting its consequences — marketers waste 21% of their marketing budgets because of bad data.
Challenges to overcome. Data from different advertising platforms lives in different places and has different structures, making it difficult and time-consuming to collect every bit of it. You have to be sure of the data you collect (by updating it retrospectively) and the security of the connector or service you use for this purpose. Also, all advertising services have regularly updated APIs, and correspondingly your connectors must also be updated. Otherwise, you run the risk of making the wrong decisions because of data collection errors. The challenges of collecting marketing data include the following:
- Fully controlling access to your data (it’s all about data security)
- Getting raw unsampled data (to avoid distorted reporting) with minimal resource costs
- Collecting the correct data (no duplication or data loss)
- Ensuring the relevance and high granularity of data
- Preparing in advance for the creation of your data storage or data lake
- Blending data from different sources without having to write SQL queries, etc.
Solution. Instead of collecting data manually, the best solution is to automatically collect data into a data warehouse from advertising services and your website using data connectors such as OWOX BI Pipeline. Then you can enrich the collected data with data from your CRM system and other sources.
We recommend using Google BigQuery for data storage. It’s the best option on the market that considers the needs of marketers. You can send raw data from your website to BigQuery and add data from your CRM system and advertising services.
There are various tools to help you pull together all this data:
- Built-in connectors from BigQuery
- Individual connectors (e. g. the Google BigQuery to Google Sheets add-on)
- Platforms that provide end-to-end solutions for data collection, processing, and visualization
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Data normalization is the process of organizing or structuring a database so all records are uniform, meaning that all data in a given field is in the same format.
Why do you need to normalize data? You’ve already done an excellent job and set up data collection on all customer touchpoints. However, your challenges are not over. Now you need to get your data in a single format and make sure it’s updated and complete.
Imagine you want some fresh juice. You take apples (cost data from Twitter Ads in dollars), pears (expenses from advertising services in euros and dollars), and oranges (data from your CRM system in pounds). You’ve already put them in one basket — that is, collected your data in one place. What about your next step? You have to:
- Wash the fruit: Make sure the data has been collected correctly and that there are no rotten apples (no sampling or duplication).
- Peel the fruit and cut it into pieces: Bring your data into a single format, one currency, etc.
Now your fruit is ready to be sent to the mixer! And your complete data is ready to please you with a consistent stream of fresh information.
Challenges to overcome. Many mistakes and difficulties can appear during data normalization. If it’s done manually, the monster comes from a heap of queries and scripts, and if something breaks or changes, everything breaks down.
As a result of all data manipulations, you should get accurate, structured information at the output: uniform tag formats, a single currency, eliminated data doubles, etc. Normalized data is great data! The main challenges are:
- Minimizing or avoiding data modification issues: Update or insert anomalies can severely impact data accuracy
- Minimizing or avoiding any undesirable insertion, updating, and deletion dependencies
In general, you have to deliver high-quality, structured data so you can focus on delivering useful insights.
Solution. Of course, when it comes to cleaning data and bringing it into a single format, analysts can do it manually using scripts and SQL. However, it’s much more convenient to use ETL services to apply automated solutions. Ideally, your chosen data connector should normalize data across all your channels:
- Clean and stabilize data and monitor its quality
- Convert currencies across different markets or shops
- Merge cost data into a single column (each market platform has different names for the same fields)
Data blending includes merging data from multiple data sources into a single dataset (usually with the help of SQL queries).
Though this approach is customary, progressive companies have started applying data modeling so as not to prepare data for each and every report. The OWOX BI team uses a data build tool (DBT) to model customer data. With this approach, data is modeled once, after which it’s easy to manage queries, build reports, and make any changes. The DBT service is very convenient and definitely the number one tool for such a task.
Why do you need to blend data? Blending data is crucial so you can get a clear overview of the ROI to identify underperforming platforms and reallocate the budget.
Unfortunately, whereas blending fruit is simple — just send the cut up pieces to a blender and voilà! the juice is ready — there is no such simple solution with data. Data comes from different sources (advertising platforms, CRM systems, etc.) and accordingly has different structures. This means that to make everything work, you have to take separate query results for different data sources and aggregate them in one dataset.
We’ve already mentioned data normalization in the previous step, but it can be performed at different levels, and we’re interested in merging data from different systems into one table and getting different levels of granularity and detail.
Challenges to overcome. Everything is quite easy when you analyze only one data source. However, the biggest challenge comes when you need to build, for example, a performance report with lots of data. This process, involving large datasets from various sources, takes a tremendous amount of time and is often simply impossible without the participation of analysts.
In addition, possible limitations in the data blending process depend heavily on the tool you’ve chosen to work with. To determine which services are best suited for your tasks and avoid overpaying, we recommend taking advantage of free trial periods and demo meetings.
Solution. As with normalizing data, specialized ETL tools will help you with blending data. Which option should you choose for data blending? It all depends on the size of your company and, accordingly, on the amount of data you work with.
- If you have one or two data sources — for example, a website and Facebook ads — it’s enough for you to use free and popular tools like Google Data Studio.
- If you have a large e-commerce project, many advertising campaigns on different platforms, and want to consider both online and offline user actions, then you cannot do without advanced services (like OWOX BI + Data Studio/Tableau/Power BI, etc.) and an analyst’s assistance. Choose services that also offer universal importing, provide high data granularity, and monitor data quality.
Note! Data Studio or any other BI tool is designed to work with report-ready datasets where data is already blended. If the BI tool merges data itself, it will work slowly and inconveniently while using more than two data sources.
Dashboard creation is the visual presentation of key performance indicators that helps you stop guessing how much you’ve spent across all your channels and report on marketing performance with confidence.
Why do you need dashboards? To understand whether your campaigns and website bring new customers and to get information about which channels work, in which growth zones it’s worth investing, and where to stop wasting your budget. In short, reports are required for two main reasons:
- To monitor the current situation and work progress
- To analyze and find out why something specific is happening
Note! Don’t forget about data visualization, which helps you understand information faster and more easily.
For ease of use, launch paid ad campaigns with pre-made dashboards that will show the actual effectiveness of your investment. If you don’t prepare reports in advance, you’ll first spend your money aimlessly, and then you will still need to prepare reports.
Challenges to overcome. Visualizing data is a regular and painstaking task that requires a lot of attention. Also, if you constantly have to wait for help from analysts, you should think about using services that will help you avoid wasting time and provide assistance to marketers in creating reports. Among the main challenges of dashboard creation are:
- Correctly visualizing the meaning of reports
- Using complete, quality data to build reports with any parameters and metrics without limitations
- Automatically updating data and changing the reporting period with ease
- Making it clear at first glance what information is presented in graphs and tables
- For complex dashboards with a large amount of data and data sources, you may often require help from your analysts, whereas they usually have other tasks that are priorities
Also, don’t forget that to get holistic performance reporting, you need to pay special attention to your choice of attribution model — it must take into account the funnel and the peculiarities of your business.
Solution. The market offers marketers lots of opportunities to build reports for every taste, from everyone’s favorite Google Sheets to complex business intelligence tools. Note that if you create ad reports manually, such as in Excel or Google Sheets, you risk your time and the quality of your data. And poor quality data, as we have already said, is the first reason for wrong decisions.
Now that you’ve gone all the way (your data is collected, cleaned, and modeled), you have to cross the finish line by connecting this data to the dashboard service and getting reports.
The tool used typically depends on the size and requirements of the business. What can be done by marketers to achieve great results?
- Collect data using ETL tools
- Use services such as Smart Data that work with modeled data and allow marketers to build any reports without ongoing assistance from analysts and based on data in Google BigQuery
- Import reports into visualization services or Google Sheets
How to solve all these challenges with OWOX BI
Managing the shifting world of digital commerce without constant monitoring is very difficult. Give yourself a gift and stop spending time on manual reporting! Sounds nice, doesn’t it?
You can get it all with OWOX BI. This service frees up your valuable time and handles:
- Data collection
- Cleaning, deduplicating, monitoring the quality of, and updating data
- Data modeling and reporting
With OWOX BI, you can collect marketing data for reports of any complexity in Google’s secure BigQuery cloud storage without the help of analysts and developers.
You no longer have to wait for reports from an analyst. Treat yourself with ready-to-use marketing dashboard templates or get a customized report based on modeled data and suited just for your business.
With the unique OWOX BI approach, you can change data sources and data structures without rewriting SQL queries and rearranging reports. This is especially relevant in the world of the new Google Analytics.
To avoid reporting challenges, you should select analytics tools that allow you to:
- Be sure the data you’re using is accurate and get better oversights of how you’re handling your data
- Avoid wasting time on multiple datasets and putting square pegs in round holes
- Make sure all your teams are aligned, using the same processes, and communicating effectively