A strong data strategy can help maximize the potential of any retail technology investment.
Red Ant CTO Dan Hartveld discusses why retail organizations need a strong data strategy to thrive and maintain customer experience
Smart retailers have realized that their data is their greatest asset in optimizing the customer experience. With the amount of data growing exponentially, retail organizations need to know where to start to maximize potential advantage. a recent Red Ant The survey revealed that retailers’ biggest challenge (for 25 percent of respondents) is data management and integration, to integrate their data estate with tools and technologies to make smart business decisions. So surely a data strategy, the most important resource for organizing data, is the ideal starting point.
However, nearly half of retailers (49 percent) say their data strategy is clear but not widely understood across the organization, and another 16 percent say it is not clear or widely understood. Added to this, 39 percent are not confident in the quality of their data, saying it is adequate but could be improved. This lack of focus on data could represent a significant barrier to successful retail transformation.
One thing that retailers are clear about is the value that data can provide. While 37 percent of retailers marked data as a priority, 40 percent said leveraging data as part of an omnichannel strategy is a top priority, and 82 percent see customer value, which is wholly dependent on the data quality, management and integration. With data at the heart of retail transformation challenges, adopting a strong data strategy is vital to being “data ready” for retail technology adoption.
Taking a data-first approach
Taking a data-first approach boils down to two things: data quality and data integration, to drive a seamless and personalized customer experience. These combined elements allow retailers to connect customers, staff, and store technology in real time through cloud platforms, as well as back-end systems such as e-commerce and CRM.
Building a strong data literacy, maturity, and culture does not happen overnight, but is supported by a data strategy that should reflect business objectives and be shared with key stakeholders across the enterprise.
Building an effective data strategy
A data strategy creates a kind of narrative in the business, around the role that data will play in the future success of an organization. It must be clear and understandable, avoiding jargon and aligned with business objectives. It should be practical and pragmatic, rather than suggesting technology in an unstructured way that is not evidence-based when it comes to value.
Make it actionable: Try to do something with this data plan, for example, to increase brand loyalty with timely identification of a customer’s needs. This is a document that should inspire the company to think differently. The data strategy will need to be reviewed as the business evolves through different stages of maturity, so make it a living document.
Your data strategy should include business goals – setting aspirations within the context of your current situation, such as your current pain points and competitor activity in your specific space. You need to establish your methodology and process for collecting different types of data across the organization, identifying what that data will help you achieve. Set out some workflows and initiatives to undertake to get there. You must have a clear roadmap with signposting from the current state to the goal. The first step must be one that a retailer can take tomorrow.
Benefits of a data-first strategy
Once the data is in order, secure and compliant, retailers can leverage this to engage with customers and build loyalty and revenue. An omnichannel retail platform brings essential retail applications together with your existing systems, content, and data into a single hub of peers and is built on retail data analytics.
Companies that use retail data analytics to improve their business tend to outperform their competitors because they gain a single customer view of the entire buying process and are able to reach them with relevant, personalized, and timely engagement. In fact, AccentCustomizing Pulse Check Survey reported that 91 percent of consumers they are more likely to shop with brands that they recognise, remember and provide relevant offers and recommendations.
Using retail data analytics as part of a customer search application can improve retail operations and increase sales in the following ways:
- Generate insights on customer behavior — Collecting data and analyzing the results helps retailers understand how their customers shop. By unifying your online and offline shopping channels, you will improve in-store customer service.
- Promote long-term loyalty and repeat purchases — Retailers can filter their data on a personal level so store associates can deliver a superior customer experience. With data recommendations, store associates can focus on increasing sales and ensuring each product is tailored to the shopper.
- Improve return on investment — Understanding the most popular products, customer demographics and recommendations means retailers can use the results of their previous campaigns, as well as current insights, to create personal and effective promotional offers.
- Manage store operations — Retailers can make predictions to help with inventory management and streamline back-office processes. Data analytics can identify product popularity, stock levels, speed of service, and average purchase value so retailers can efficiently manage in-store activity.
- Greater customer retention — An omnichannel retail strategy that focuses on personalizing the shopping experience and bypassing the limitations of a single channel will encourage shoppers to return to the store or buy from the brand online.
- Improved operational efficiency — An omnichannel platform that unifies multiple operations will shed light on any systems that have become redundant, allowing brands to identify any avoidable drains on operating budget.
It’s about data quality, not quantity
There is no point in having large amounts of data if you are not using it correctly. Data must be compatible, of high quality and integrated with all existing, new and third-party systems to maximize the potential of any investment in retail technology, increase long-term customer loyalty and revenue.
An effective data strategy will serve as the foundation for all of this, so that customers and store associates alike can be empowered with the right product and customer data. Digital leaders will ensure constant communication across all customer channels and the success of your planned projects.
What Retail Can Learn From Supply Chain Disruption: Suzette Meadows, Principal Consultant, Contact Center/Unified Communications at Exponential-e, discusses what retailers can learn from supply chain disruption.
Four Key Data Management Steps for Effective ESG Reporting – Patrick McCarthy, chief revenue officer at Precisely, provides four key steps for using data management in ESG reporting processes.