Three Modern Sales Analytics Use Cases
- John Morrell
- October 9, 2019
Data and analytics are widely touted as the new “black gold” of the 21st century. It’s not necessarily an imperfect analogy, but it does somewhat miss the scope of the value data offers. From customer experience to finance to advertising, data can enhance a plethora of business elements. One of the best ways to use data lies in sales analytics.
You already use sales measurements to optimize sales efforts, promotions, and profit margin in an aggregate way. If you really want to accelerate your business’s success, go further by using the following three modern sales analytics use cases.
Upselling and cross-selling
If there isn’t a saying like “the best customers are ones you already have” in sales yet, there should be (remember, you heard it here first). You are 60% to 70% more likely to sell to an existing customer than a new prospect.
Sales analytics can play a crucial role in upselling current customers on new products and services. Detailed analytics on the total customer journey and customer experience over time (not just point-in-time journeys and experiences) can not only reveal long-term patterns. Still, it can help map customers into various states in that journey. In our last blog, we discussed using Customer Lifetime Value as a long-term metric. Also, looking at the products purchased, repeat purchases, when purchases occur, and each purchase’s experience can map to where the customer resides in their overall journey and spot opportunities for upselling and cross-selling.
Upsell and cross-sell analytics requires bringing together a variety of different analytic assets that reveal specific aspects of the picture, including
- Individual sales over time
- Web analytics
- Campaign touchpoints and influences
- Customer experience data points
- Social media posts
- 3rdparty data providers
This data could reside in various systems: analytics, data warehouses, web analytics, CRM, marketing, CX, social media, and external service providers. Bringing together the various assets and specific data points they represent, then aggregating them into a bigger, more detailed picture, will help reveal repeat sales patterns, the influences on repeat sales journeys, and where customers are in their journey. This aggregate view will reveal upsell and cross-sell opportunities and offer recommended actions to capture those opportunities.
Individual local markets can vary greatly in terms of demographics, product mix, price elasticity, sales channels, marketing channels, and more. While every organization strives for brand consistency, tailoring sales efforts to local markets can uplift sales in each market and maximize overall sales.
Leading firms are using sales analytics to understand sales conversion at a micro level to structure sales initiatives, promotional campaigns, and even stocking levels that best fit the specific local markets. And it is not simply about analyzing your own data. For example, understanding the impact of local influencers and specifically which local influencers can be crucial.
Determining how to localize sales tactics and campaigns requires using different analytic assets, including
- Local sales by the individual market at a detailed level
- Localized web analytics
- Results from localized services such as Yelp, Google Maps, and more
- Pricing and promotions at a local level and influence on sales
- Customer experience data points in individual markets
- Localized social media posts, including those of local market influencers
- 3rdparty data providers
These analytic assets will reside in multiple systems and locations – on-premises, cloud, SaaS, or external web services solutions. The objective is to bring together all the assets into an aggregate view, slice up the data by individual market, then perform analytics on the individual markets to determine the best actions and promotions to drive sales at a local level.
Pricing and Price Elasticity
A price-management strategy can improve your profit margin by 2% to 7%. Perhaps the most effective way to build a thorough price-management plan is to use detailed level analytics on sales, promotions, and competitors. One can also use scenario modeling to predict the future impact of prices or deals on sales, based on how current prices and competitors’ offers are affecting sales now.
For example, your current sales analytics likely can show your most popular products, the average price sold, and the raw profit margin. But digging deeper into a broader set of data could answers questions such as:
- What was the sales journey by-product and pricing level to determine the effort and cost to get those sales?
- What were sales and pricing levels with no promotions and across various promotional campaigns to determine optimal pricing within promotions?
- What are the demographics of people that buy at various price levels to determine personalized pricing strategies?
- How do our actual prices and promotions compare to competitors to determine better pricing strategies and use of promotions?
The analytic assets to answer these questions will reside in several distributed locations and come in the form of different assets, many of which are similar to those previously mentioned in the two prior use cases. Aggregating these assets into a “single plane of glass” can help analysts quickly slice and analyze the data in various forms to answer the aforementioned questions and deliver better pricing and promotion strategies.
No-code Data Transformation Simplifies the Process
Data transformation can be a difficult and complex part of the process when building sales analytics. You may have solved the simple EL part of the process through data loader tools that can extract data from sources such as Salesforce, Hubspot, Google Analytics, and Adobe Analytics and load it into your Snowflake data cloud. But that’s the simple part. Transforming this large, diverse, and complex set of data into something consumable in your dashboard is the difficult part. At least until now!!!
The Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake. The easy-to-use, multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data. Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance. Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.
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