Making better, faster decisions with data is mission-critical for retailers. Competition in the retail industry is only getting fiercer, and the omnichannel landscape has further increased pressures that retailers face. Ecommerce has sprung new digital transformation initiatives, spurring the rapid growth of data. Fifty-eight percent of retail sales will be digitally impacted by 2023. However, possessing a treasure trove of sales and customer data is one thing; fully harnessing it is quite another. Unfortunately, for many organizations, accessing and analyzing large, complex data sets is no easy feat. In fact, only 13% of organizations say they’re making the most of their available customer data. Here are 3 reasons why retailers are struggling to maximize their data:
1. Big data is often inaccessible to business users
Many existing business intelligence solutions require advanced skills in SQL, leaving retail teams to field requests to experts to query data. This causes bottlenecks and time delays, resulting in slow decision making and undermining retailers’ ability to provide exceptional customer experiences. For example, when it comes to inventory management, shoppers expect to be able to purchase the products they want, when they want them, on the channels they prefer, but reliance on specialized analysts gets in the way of quickly meeting customer needs.
2. Traditional spreadsheets don’t scale for big data
Many retail teams still use traditional spreadsheets as their main tool across all activities, but they don’t scale for big data—the higher the volume of data, the slower the spreadsheet processes, leading to more chances for the data to become corrupted and making it practically impossible to glean valuable insights.
3. Extracting, moving, and combining data is cumbersome and introduces the risk of poor data
The time teams spend on non-value-added tasks like manual data gathering, consolidation, verification, and formatting leaves little room for the type of analysis and strategic planning retailers need to turn data into dollars.
In addition, these workflows bring with them the risks of accidental data manipulation and stale data. Incorrect data leads to inaccurate forecasting and flawed decision making which can have costly consequences. Inaccuracy also impacts your ability to deliver personalized customer experiences.
Fortunately, retailers can now make the most of their data with the combined power of Google Sheets and BigQuery
Retail teams can now bring the power and scale of BigQuery and the familiarity of Google Sheets together with Connected Sheets. Connected Sheets helps retailers harness and collaborate on massive amounts of retail data, making it easier to understand and quickly respond to today’s dynamic landscape. It offers familiar tools like pivot tables, charts, and formulas so you can analyze and visualize large datasets to improve the retail value chain.
Connected Sheets helps retailers drive data-driven decision making by:
1. Democratizing data analytics
With no SQL skills required, Connected Sheets frees everyday business users from the time delays and bottlenecks associated with legacy workflows. Self-service analytics empowers teams by giving them the ability to uncover valuable insights that drive profitable decision making. For example, Connected Sheets enables anyone to easily bring together data from in-store, online, and mobile sources, giving retailers the insights they need to optimize their omnichannel strategy. Compare all your sales channels in the context of your marketing and make data-driven decisions so you can shift your spend and focus for maximum impact, or measure and track eCommerce KPIs from revenue, to shopping cart abandonment, to conversion rates and lifetime customer values.
2. Accelerating speed to insights
Say “goodbye” to manual data gathering. With Connected Sheets, retailers can glean actionable insights faster and quickly respond to market opportunities and areas for improvement with the ability to access, analyze, and visualize billions of rows of BigQuery data in Google Sheets with no performance issues. With improved accuracy and near real-time visibility into inventory levels and product movement, retailers can optimize business processes and achieve frictionless store operations swiftly. For example, quickly pinpoint the source of issues and complaints by analyzing customer feedback and locating supply chain issues. Or, use inventory and POS data to do better on-shelf inventory tracking, clienteling, and supply chain management.
3. Ensuring you have the most-up-to-date data
Retailers need to engage with their customers and adapt to their demands in real time. Connected Sheets makes this easy by enabling event-based, real-time decision making. The live connection to BigQuery means data is easy to refresh and always up-to-date, so you can ensure accurate predictions and demand forecasts, and deliver on-the-mark, personalized customer experiences. For example, drive customer acquisition and retention by accessing and analyzing user behavior data to discover more about what encourages your customers to make buying decisions or quickly update prices based on supply and demand.
Connected Sheets also enables you to spend more time on analysis and strategic planning with scheduled refreshes that improve workflows and streamline reporting and dashboard creation. For example, use scheduled refreshes to automate custom inventory reports for store managers or partners daily (or as often as you’d like).
4. Enabling secure sharing and collaboration
With Google Workspace’s enterprise-grade security, sharing and collaborating on insights is risk-free and easy. You can control permissions to limit who can view, edit, or share, and users can perform analysis in the sheet without fear of jeopardizing the integrity of the data in BigQuery. Share and collaborate on insights cross-functionally to build a data-driven culture and tap into collective intelligence. For example, share dashboards with marketing to help determine the best time to run promotions.
