How to Create a Business Intelligence Concept for Your Company
We show you what advantages a BI concept has and how you can develop it for yourself.
- What is meant by a Business Intelligence concept?
- What benefits does a BI concept offer?
- What are the requirements of a Business-Intelligence concept?
- How do you create a BI concept?
- What technologies and application areas are there for Business-Intelligence concepts?
- Why you can't avoid a BI concept
Nowadays, data is indispensable in e-commerce. However, all the data is relatively useless if it's not properly processed. This is where Business Intelligence (BI) comes into play. As a relatively broad collective term, one can roughly say that Business Analytics, Data Mining, data infrastructure, data visualization and also data tools fall under Business Intelligence. In short, and in the context of this article, BI covers all processes from the collection, storage, representation to the evaluation of business data.
Our guest author Florian Langer explains in this article what a Business Intelligence concept is, what advantages it has, and how to implement it.
What is meant by a Business Intelligence concept?
There are four important steps that a Business Intelligence concept goes through to convert raw data into easily digestible insights that everyone in the company can use.
The first three - data collection, analysis, and visualization - form the basis for the last step of decision-making. Before the deployment of BI, companies had to do much of their analysis manually, but BI tools automate many processes and save you a lot of time and effort. In this can, BI can help, for example, create successful Facebook advertising campaigns.
The following graphic shows a very lightweight ETL process (Extract, Transform, Load) that takes into account each step of the process.
Step 1: Collect and Transform data from various sources
Business Intelligence tools typically use the ETL method to merge structured and unstructured data from various sources. These data are then transformed and reshaped before being stored in a central location. This allows applications to easily analyze and query them as a comprehensive dataset.
An example of this step could be that a date is always in the same format, that all weeks are “ISO-Weeks” or that all email providers' domains start with www.
Step 2: Uncovering trends and patterns
Data Mining or Data Discovery typically uses automation for the rapid analysis of data to find patterns and outliers that provide information about the current state of the business. BI tools often provide various types of data modeling and analysis - including exploratory, descriptive, statistical, and predictive analysis. With these, data can be further investigated, trends can be predicted, and recommendations can be made.
Step 3: Data visualization for the presentation of results
In Business Intelligence reporting, data visualizations are used to make the results more understandable and easier to share. BI reporting methods include interactive data dashboards, graphs, charts, and maps that show users what is currently happening in the company.
Step 4: Use the insights in real time
The review of current and historical data in connection with business activities gives companies the ability to quickly move from insights to actions. Business Intelligence enables both the adjustment of the strategy in real time and long-term strategic changes. These can eliminate inefficiencies, adapt to market changes, correct supply problems, and solve customer problems.
The six most popular Business Intelligence tools based on verified reviews and experiences from the OMR community massively simplify the above process and with a few clicks directly integrate at the data source, without needing to use an extra tool for the intermediate steps.
A comparison between Tableau and Mircosoft Power BI, or in the area Self-service-BI with Tableau and SAP can also be found on OMR Reviews.
What benefits does a BI concept offer?
Business-Intelligence-Tools help you to analyze the data that is relevant to your business in real-time and automated. This not only avoids inefficiencies, but you are also able to detect potential problems early. In addition, Business-Intelligence tools offer the possibility to identify new sources of income and / or new areas for growth through pattern recognition.
Specific benefits that companies experience through the use of Business-Intelligence tools include:
- Higher efficiency of operational processes
- Insights into customer behavior and buying habits
- Accurate tracking of sales, marketing, and financial performance
- Clear benchmarks based on historical, current, and forecasted data
- Immediate alerts about data anomalies and customer problems
- Analyses that can be used cross-departmentally in real-time
If we look at an online shop for example, our BI tool helps to identify customer segments and to control differentiated offers based on these different groups of buyers. Because all data converge centrally in one place, we have all the information available on a transaction level that we need to push customer loyalty and brand equity.
Example of customer segments
Because we know how many and which touches customers had, we would influence the RPR (repeat purchase rate) of the individual segments directly by playing certain channels more heavily in the respective cohort. We would also find out which segments of the audience have a high PRR (Product Return Rate) and here react accordingly with measures that either exclude this segment or here provide educational content that once again clarifies the added value of our product.
In addition, we would be able to make data-based and business-intelligent (pun-intended) statements about our abandoned shopping carts (CAR - Cart Abandonment Rate). Maybe the ad with the yellow background is the one that clicks the best, but has the highest dropout rate, while the one with the blue background seems to be very expensive but delivers the highest Average Order Value (AOR).
Since we also have all the information about the customer journey and the value chain, we are also able to adjust our prices dynamically to the respective segments. Here, machine learning algorithms help us, which are able to predict the future performance of the segments.
What are the requirements of a Business-Intelligence concept?
There are thousands of SaaS tools and data sources on the market, so it's indispensable that all the data from your ecosystem also goes into your Business-Intelligence tool. Cleanly implemented tracking is therefore essential for the start of the Business-Intelligence journey.
Once the basic structure of your data infrastructure is in place, you not only have transparency over the data pipeline but can also define when and how often the data should be updated. In addition, it becomes easier to detect errors or bugs and to fix them quickly.
How do you create a BI concept?
Especially in the area of B2B SaaS, we often face the challenge of distributing budgets at the right time to the right channel. For this, it's indispensable to know the customer journey precisely and in detail.
Knowing where potential customers purchase, how they behave in the funnel or when they consume which content are all important puzzle pieces of the purchasing decision. The journey in the B2B area, however, is not always straightforward and the composition can present a challenge.
We also face the challenge that end user of our tools are not necessarily also people who ultimately make the transaction or the purchase decision. In addition, visits from the tech team are very likely also part of the journey.
So we have data from various tools that we need to bring together to determine which of our activities are successful and have a positive impact on the pipeline generated by marketing.
The problem is that these tools are almost without exception silo-like in structure. This means that the data on the customer journey is disjointed. So every attempt to bring them under one roof is a challenge.
The BI concept should therefore consolidate all revenue-related data in one place. This means that both data from on-site tracking and from your entire ecosystem - CRM, advertising platforms, automation tools, etc. - are brought together.
Once all these data are merged and cleaned - to ensure that there are no duplicates or empty values - the data are run through the multi-touch attribution modeling.
The multi-touch attribution modeling then traces every touch of the customer journey and assigns it to the pipeline and the generated revenue.
This means that the actual first touch is identified and credited, regardless of when the data is collected. Ultimately, this means that our BI concept should enable us to determine exactly:
- which channel the lead was actually acquired through
- which channel led to this one moving further in the decision-making process
- what exactly happened before someone from your customer's implementation team looked at the implementation documentation
In combination with UTM and cookie best practice, we finally get a holistic and complete insight into the acquisition!
What technologies and application areas are there for Business-Intelligence concepts?
If you've worked a bit with Google Analytics and the Tag Manager, you know how easy it is to create, track, and attribute a conversion event. However, you might only be able to really assign the last touchpoint because the results are aggregated.
That's not enough, because such a journey can become very complex. Taking important strategic decisions based on the last touch can involve risks.
Why? Let's take talent acquisition for example. Most companies have a career page that invites new talents to apply. Of course, you very much want to know how and from which source you get the best applications, so you can increase your headcount as efficiently as possible.
This could be what a typical candidate journey looks like:
Session 1 on 02/09/2022 | Session 2 on 28/09/2022 | Session 3 on 03/10/2022 |
|
|
|
|
|
|
The person on the other side of the screen has had 3 sessions on your site, each from different channels (Linkedin Paid, Organic, and Direct). You've also got two milestones on the journey before the application was submitted. A podcast was listened to and a job posting was read.
Last Touch Attribution Models would attribute the application 100% to the “direct” channel. So you would not be able to correlate your conversion with the previous sessions (from Linkedin and organic search) or the events (podcast and job page). So you would not get the whole truth.
Why you can't avoid a BI concept
BI is more than just software – it's a way to get a holistic overview of all relevant business data in real-time. Implementing BI offers a variety of advantages, from better analyses to increasing competitive advantage.
The areas of application for BI are manifold. From shop to recruiting and from B2B SaaS to banking: If you want a transparent representation of all major milestones of your customer journey and the respective channels, you can't avoid creating a BI concept, implementing it, and then continually optimizing it.
In der DECAID-Group ist Florian der Ansprechpartner Nummer eins für sämtliche Go-To-Market-Belange. Er kreiert Content für die decaid.academy und Le Wagon, mit Schwerpunkt auf Data Analytics und Growth, um Firmen die effektive Nutzung innovativer Lösungen zu ermöglichen.
