Retail companies have always relied on data to make informed decisions. The thing is that you can’t succeed without knowing how customers respond to your marketing campaigns, whether product sales meet established KPIs, and what areas need improvement.
While data analysis is one of the mainstays of fact-based decisions, you also need to present these insights in a digestible format. That’s where retail business intelligence can help.
Retail BI denotes the process of collecting, aggregating, mining, interpreting and using data from various sources for informed decision-making across various business areas, such as:
- Inventory management and procurement
- Product pricing
- Marketing, sales and customer service
- Customer intelligence, and so on
However, despite seeing all the advantages, many companies still struggle with BI adoption for several reasons. Let’s take a closer look at some of the most widespread ones and ways to overcome them.
1. The Need to Combine Data From Scattered Systems
The outcome of data analysis depends on the ability to integrate information from diverse sources. With so many tools, from CRM and ERP software to marketing automation platforms and POSs, a retail business should operate daily, it can be challenging to process data generated by all of them.
What is more, these systems can provide data in different formats, volumes, modes, and be non-systematic.
Add to this the probability of data silos as information within the same company can remain isolated across departments. Even with BI software in place, an incomplete view of business operations results in insufficient reporting.
Solution
Create a centralized data repository like a data warehouse (DWH) to store cleansed and standardized data from multiple corporate systems. To keep raw information of huge volumes, you can also employ a data lake. This way, you develop a single source of truth.
Besides that, you have to ensure data uniformity, including definitions for key performance indicators (KPIs) and metrics, as well as create robust data models for accurate, cross-functional analysis. Leverage ETL/ELT tools, if applicable, to extract information, transform it to a common format, and load it to the DWH.
To encourage people to share information across departments and generate more accurate BI reports, it’s also crucial to cultivate a culture of cooperation, communication, and data sharing.
2. Poor Data Quality
Another reason for dissatisfaction with BI implementation is data quality. When you integrate disparate systems, there is a significant risk of inaccuracies, duplicates and outdated information.
Solution
Put data quality front and center of your BI implementation initiative. It’s easier to check what data is fed to the system rather than to rectify mistakes in the final reports, let alone deal with the consequences of the decisions made on their basis. Here is what your data management strategy should involve:
- Hiring BI experts with hands-on experience in the retail BI solutions to select relevant data sources and data cleaning approaches
- Devising data quality standards and protocols
- Implementing robust data cleansing tools or enabling data cleansing capabilities within your BI solution
- Assigning data stewardship roles for accountability
- Introducing a company-wide data dictionary and standardizing data entry
- Continuously monitoring, validating, and maintaining data quality with audits
3. Insufficient Technical Expertise
Complexity of BI tools chosen by the company can be another reason for retail BI failure.
The thing is that if it’s difficult to grasp, the tool will be ditched in favor of the good old Excel or any other familiar software.
Solution
There are two ways to surmount this challenge: employing intuitive BI tools and raising the technical literacy of your teams.
First, opt for the software that facilitates a seamless user experience. This may include a user-friendly interface, intuitive navigation, custom dashboards, and interactive data visualization. Additionally, self-service capabilities are a great option for employees to generate reports without much involvement from IT specialists. These tools are Microsoft Power BI, Tableau, Looker, and so on.
The next step should be investing in team education, e.g., dedicated courses, workshops, and tutorials. Request BI consulting services provided by specialized agencies to eliminate issues and concerns arising during the BI implementation process. Also, make sure the BI platform’s vendor offers ad hoc and proactive customer service assistance.
4. Lack of Employee Buy-In
Humans naturally have their ingrained habits and avoid any change. This is also true when adopting new work approaches. Resistance to new software can stem from the lack of understanding of its real value.
Being unfamiliar with BI tools is one reason why people don’t want to use them. Some fear that automation the BI software enables will leave them jobless; others may not share the vision of the C-suite about the impact of BI software on productivity and the quality of decisions.
Solution
The introduction of new procedures calls for a change in the work culture. As part of the employee onboarding process, outline BI implementation goals and benefits, present a proof of concept, and guide people through the process of report generation or BI dashboard creation.
5. High Risks of Cyberattacks and Data Breaches
Retail business intelligence tools ingest large volumes of sensitive information, such as:
- Customer data (personal details, payment information, behavior insights, and purchase history)
- Operational data (sales and transactions, inventory, supply chain, and employee information)
- Proprietary business data (financial records and performance metrics)
All of this can be useful for fraudsters to steal, utilize, or demand a ransom for. With the increased usage of artificial intelligence, cybercriminals are getting more sophisticated in their attacks.
Solution
To avoid lawsuits and financial and reputational damage, follow these data governance and security best practices when implementing a BI solution:
- Implement end-to-end encryption
- Use role-based access control (RBAC)
- Conduct regular security audits and penetration testing
- Implement multi-factor authentication (MFA)
- Leverage tools for continuous monitoring and real-time threat detection
- Obfuscate information with data masking
- Perform compliance audits to ensure your data management practices adhere to all applicable data privacy regulations and standards (e.g., GDPR, CCPA)
Final Thoughts
According to Mordor Intelligence, the retail business intelligence market is set to expand by 22% and hit $7.7 billion in 2029. Despite this, there are some problems left to resolve, and retailers should take them into account when implementing business intelligence.
We’ve mentioned the top five stumbling blocks on the path toward BI implementation, including:
- Information coming from disparate systems
- Data quality concerns
- Low user engagement and employee commitment
- The need to train the staff and hire IT professionals for user training and support
- The requirement to take cybersecurity measures
Of course, there are many more barriers. However, don’t let them stop you from implementing retail BI software into your IT environment, as it promises much more productivity, scalability and revenue growth opportunities down the road.
Invest in robust and user-friendly retail BI software, partner with a reliable team, and start making business decisions based on real-life data rather than guesses and gut feelings.
