Data-driven organizations are focusing on leveraging analytics for many reasons: to deliver exceptional customer experience, streamline their operations and foster innovation with new products and services. Retailers turn to analytics to drive business value in a variety of ways, including supply chain optimization, inventory management, price optimization and much more. In all these scenarios, maintaining the quality of data used to derive the insights remains the utmost concern for retailers.
Insights from various heterogeneous data sources across on-premises and cloud sources provide retailers with a comprehensive view of their business across customer, sales, marketing and operations. To get a complete picture of the customer journey and make an informed decision, retailers usually append a customer’s offline purchasing patterns with their online activity. Trends in the market keep changing, and so retailers need to be able to continuously analyze current trends and make any necessary changes in their recommendation algorithm. Offline data about certain events also assist in merchandising and better staffing for the stores. However, any data quality problems are potentially multiplied each time data is combined and transformed. This makes it imperative for retailers to closely assess the quality of their enterprise data and apply relevant quality improvement techniques before using it for valuable insights.
Data quality matters, not only for the retail analysts who are responsible for their line of business growth but also for data scientists who use data as the fuel to train their models and develop algorithms for use cases such as product recommendation, churn analytics and more.
Here are some of the scenarios that illustrate why retailers must assess data quality before performing any analytics or AI/ML initiatives for driving business value.
1. Supply chain optimization. Retail companies that entice prospective customers with add-on services such as free shipping are spending a lot in order to create brand differentiation. However, poor data quality — for example, an address that’s missing apartment number information or shows an invalid zip code — will cost a retailer not only in terms of a late or failed delivery, but because any shipping problems will likely lead to poor customer experience and product returns.
Instead of allocating resources to fulfill every request, retailers could instead benefit from detailed analytics that would preemptively identify which addresses are incorrect and whether an address element needs to be corrected. Based on the proper data quality assessment, retailers can optimize their supply chain and save millions by avoiding scheduled deliveries at these addresses.
Keeping customer address data accurate and up to date can also help retailers choose the best locations for their stores. They can perform customer traffic analysis and see the areas where most of their orders are placed. A simple cost-benefit analysis can then be done to see where their stores should be located. Enriching this data with up-to-date competitor store data can also help retailers pick the best site to serve their customers. These enriched data sets can be leveraged to further optimize the supply chain by providing predictive analytics to efficiently staff the stores during peak hours or a busy season.
Some retailers even leverage data on traffic patterns and weather conditions to perform predictive analytics and learn the best route to deliver in least possible time. It is evident that supply chain analytics is helping businesses thrive and the quality of data leveraged for these analytics is the linchpin to it.
2. Inventory optimization and management. Consumer trends are now changing more frequently, and retailers need to keep up to serve them better. Retailers need to stock certain items based on seasonality and need to keep just the right inventory to avoid losses from unsold goods or highly discounted sales. The accuracy of forecasting wholly depends on whether the customer data and third-party data sources are complete and accurate in all aspects. Retailers can also leverage past sales data to forecast how much inventory they’ll need to have on hand and how much space they will need to stock their items — or even how many people they would need to staff their stores. That is why keeping the health of the data quality at a high level has become imperative for retailers.
3. Increased effectiveness of marketing campaigns. Adding relevant context to the data helps with data enrichment and improves data quality levels. Enriched information about customer behavior, their spending pattern or preferred time to shop helps retailers target the right consumers at the right time. It minimizes the cost to run various marketing campaigns, and instead targets only the most relevant customers, resulting in an increased ROI. Enriched customer data about their preferences can also help retailers provide exceptional customer service, increase sales and ideate new products and services for consumers. For example, retailers will offer discounts to salaried individuals during the first week of the month around the time of their paycheck. However, they can tailor their offer more effectively if they have enriched information, such as which credit card a particular customer uses.
Price optimization: Insights from demographics and purchasing power of different consumers can also help retailers sell their products at an optimal price. As an example, for a group of university students they can keep prices lower and initiate a loyalty or referral program, whereas for a group of salaried individuals/corporate accounts they can consider offers like bulk discounts. However, this model is successful only when you are working with the right and most relevant information available. Any exception — stale, invalid or inaccurate data — may lead to business disruption as it is directly related to the pricing and ultimately your bottom line.
4. Product improvement with customer reviews. For retailers, it is important to focus on the feedback customers are posting about their products, as most customers look at product reviews before making any purchase decision. Analyzing these reviews help the retailer make necessary product enhancements and tailor their product strategy accordingly. However, any inconsistency in the reviews must be uncovered before the reviews are used as a basis for any product enhancements. For example, an unhappy customer may provide too many bad reviews from various channels, which may affect the product manager’s decision. The reviews must be at least checked for uniqueness in this case, so that you can capture the right amount of feedback and weight it appropriately.
5. Improved brand value with data consistency. Consumers expect a consistent experience across all touchpoints when retailers are engaging new channels for promotion. Retailers need to make sure that the insights or information available to consumers through various data analytics techniques are similar across every channel. It may be disconcerting for consumers if retailers have different visual merchandising in their online and offline stores. Any product data or even the customer data (such as their orders, favorite products or any personalized view) needs to be similar across channels.
For example, retailers can leverage real-time streaming data from various social media handles and present a sentiment analysis to the consumers about their products and services. However, they must make sure that the data they are leveraging for this analysis are consistent, complete and accurate. This necessitates retailers to focus on data consistency, an important metric of data quality, to provide a unified omni channel experience for their customers.
Maintaining data quality is a continuous task, and it should include looking at the data source itself. Otherwise, retailers will end up having to pay higher prices to correct it at a later stage. Your reason for assessing data quality is not necessarily limited to providing superior customer experience or improved revenue (although those are important rationales) — maintaining data quality will improve your bottom line. Suppliers form an integral part of a retail business and companies do a lot of due diligence to select the best vendor for procuring their raw material or supplying and distributing their products. Up to date, accurate and complete data about any vendor will certainly help retailers choose the best suppliers for their business need.
Gartner® predicts that “by 2022, 70% of organizations will rigorously track data quality levels via metrics, improving it by 60% to significantly reduce operational risks and costs.”1
With the unparalleled revolution going on in the retail landscape, retailers should look to provide the ever-demanding consumer with much-needed flexibility. Assessing the data quality issues earlier in the journey and addressing them quickly can help retailers gain interconnected insights that will drive their business growth. Start today with a free 30-day trial of Cloud Data Quality, an integral part of Informatica Intelligent Data Management Cloud, which can quickly identify and resolve data quality issues without any dependencies.
1 Source: Smarter with Gartner, “How to Improve Your Data Quality,” July 14, 2021, https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality
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