Today retailers have more channels to reach and sell to customers than ever before, and shoppers are sharing more information about themselves, their preferences and their purchases across every channel. With all this opportunity comes endless amounts of data.
Often this data is collected and stored in spreadsheets, and retailers often face more problems in making sense of all the data living in their spreadsheets as they attempt to build charts and graphs or complete more advanced data analysis capabilities.
Here are three tips to effectively leverage spreadsheet data to inform business decisions, pivot to demand, and create a presence where your audience is.
Tip 1: Adopt self-service analytics
Spreadsheets are one of the most common, pervasive tools in the modern office suite, with the average user spending more than 20 hours a month within them. However, despite their widespread use, most people struggle with the data living in their spreadsheets. In fact, one in every three Australians (34%) struggle to make sense of the data that lives there.
Too often, retailers and their marketing teams are staring into a spreadsheet full of data where knowledge is siloed, and customers, products and campaigns are out of reach. So, you might ask, what is self-service analytics? And how can it help?
Self-service analytics can allow any user to instantly search to explore data, visualise insights, and share those with others to do things like create efficient, delightful marketing campaigns or analyse sales data by specific product to inform stock decisions. Whether you’re in e-commerce, merchandising, finance, or store operations, anyone can analyse billions of rows of data from their spreadsheet data—online sales, social media conversations, and inventory sales—in seconds with search & AI-driven analytics.
For example, with search-powered analytics, users can ask questions of their data directly and examine the connection between manufacturing quotas, sales forecasts and distribution instantly and as it’s happening, ultimately, having a direct impact on the company’s bottom line.
Tip 2: Embrace data visualisation
We have entered a new era in sales forecasting with analysis and visualisation. To make the most of your data, you need to be able to visualise it properly. Data visualisation makes data insights more accessible to a user at any proficiency level.
Having visuals gives you an instant understanding of how each element compares, taking a dive into relevant trends and giving context to your numbers. It can reveal patterns in complex data that would otherwise be hard to understand.
Such services where it requires no data modelling, technical skills or existing architecture can save a great amount of time and resources. The subject matter expert can dive right in, without the need of support from an analyst. Having visualisations of our data allows users to see patterns, which may otherwise go unnoticed or get lost among other things, and often delivers faster insight to action.
Tip 3: Unlock value
Retail companies produce data across many departments and levels of the organisation. Everything from sales transactions and inventory levels to shipment details and even product performance telematics.
Insights are powerful, but on their own, they have limited value. Businesses are built on actions, not information. Instead of relying on people to act on data by looking at spreadsheets, you can bring insights into operational workflows to drive smarter actions at scale.
If you have insights about your most valuable customers, or customers that are most likely to churn, bring them automatically into your marketing campaigns and customer support workflows to drive higher value for your business. By unlocking value with your data, you can turn shoppers into loyal customers and improve the shopping experience, increase fill rates, reduce overstocks and improve inventory turns.
Every retailer engages with data in some capacity, whether to measure and optimise campaigns, deliver exceptional customer experiences, or shape products and services. These three principles in making the most of your spreadsheet data, also apply for retailers that operate with a modern data stack and have their data living in Snowflake, Google BigQuery, Amazon Redshift or other cloud data platforms. The common denominator? Search and AI-powered analytics that are easy for anyone, from C-Suite to fashion retail buyer, to social media specialist, to store manager, to consume and uncover insights that drive action.
Through self-service analytics solutions, retailers can have a solid understanding of their data and how it compares against trending topics, customer behaviour and buying patterns to be able to make informed and impactful business decisions.
Because suddenly, insights from your data aren’t just something “out there”. They’re right where you need them, helping you make informed decisions to take action.