Sales and marketing analytics are essential to unlocking commercially relevant insights, increasing revenue and profitability, and improving brand perception. With the help of the right analytics, you can uncover new markets, new audience niches, areas for future development and much more. Let’s look at what I believe are the best and most important sales and marketing analytics that can help any business grow and succeed. This post builds on my article on the key business analytics tools, which might make good additional background reading.
Unmet need analytics
Business is all about meeting the needs of customers. Unmet need analytics is the process of uncovering whether there are any unmet needs around your product or service or within your market which you could meet to increase customer satisfaction and revenue. Useful tools for unmet need analytics include product reviews, qualitative surveys, focus groups and interviews. You could also use tools like Google Trends to help identify what customers are searching for.
Tip: It is now very cheap and fast to ask your customers questions. Create forums and online focus groups, or invite customers to like your Facebook page and join a feedback group.
Market size analytics
If you don’t understand the size and potential of your market you can easily jump to conclusions about how viable your business proposition is. Market size analytics is the process of working out how large the market is for your products and services, and whether there is sufficient growth potential. The size of the market is measured in terms of volume (how many units sold), value (money spent in that market) or frequency (how often a product or service is sold). Useful data includes government data, trade association data, financial data from competitors, and customer surveys.
Tip: Just because a market is large doesn’t mean it’s profitable – especially if most of the customers that want a particular product or service already have one and are unlikely to want another.
Understanding demand is essential in order to remain competitive. Demand forecasting is an area of predictive analytics that seeks to estimate the quantity of a product or service your consumers are likely to buy. It goes beyond educated guesses and looks at historical sales data or current data from test markets. Analytic techniques such as time series analysis can be very useful here.
Tip: The data that you use for demand forecasting must be clean and accurate. If it is not then the results will always be skewed and could lead you down the wrong path.
Market trend analytics
Every business needs to know the direction its market is heading in. Market trend analytics is a process of establishing whether a market is growing, stagnant or in decline and how fast that movement is occurring. Understanding market size is important but knowing whether that market is trending up or down is also vital. To monitor market trends you can run business experiments or scenario analysis to see what the market would look like and how it would impact your business in either a growing, stagnating or growth market. Customer surveys and focus groups can also help.
Tip: Stay mindful of the external environment, such as changes to legislation and social expectations.
Traditionally we’ve been told that we need to understand our customers so that we know what they look like and can find more people like them. And whilst that makes sense there is another group that could be even more important – the non-customer! Non-customer analytics is about understanding what people who are currently not your customers think about your product, services or brand. By identifying who is not buying from you (and why), you can expand your market to include those individuals. If you want to know why people are not buying your product or service, you need to ask them: interviews, questionnaires and focus groups can help.
Tip: It can be remarkably easy to get feedback from people who are not your customers using the power of social media.
Your business does not exist in a vacuum. Competitor analytics is important for marketing and strategic planning by identifying who your real competitors are, and how they are positioned in the market and in relation to your business. By understanding their strengths and weaknesses you can identify opportunities to exploit and threats to navigate. There are many ways of gathering competitor data, such as business journals and newspapers, annual reports, product brochures and marketing activity. You could even have an employee, friend or family member buy a product or service from your key competitors and assess their experience.
Tip: The most useful tip for competitor analytics is to do it! Sadly, most businesses don’t.
What if you could find out exactly how much your customers would pay for your product ahead of time? Pricing analytics is the process that delivers that outcome. In short, it involves analysing price sensitivity in market segments and is especially useful in highly competitive markets where everything that can be done has been done. Pricing analytics requires data mining and the development of forecasting models and algorithms. It also often involves multiple, concurrent business experiments that can be run quickly and easily so you can measure what is likely to happen with each price change.
Tip: If you use pricing analytics to improve revenue, make sure you improve the value you add to customers that are paying a little more.
Marketing And Sales Channel Analytics
There are literally hundreds of possible channels and ways to market and sell your products and services.Marketing and sales channel analytics allows you to assess the different channels available to you and establish which are the most effective. It is likely you will reach different segments of your market via different channels but is it still good to know which ones are working and which are less effective. For each of your current marketing and sales channels and any potential as yet unused channels you will need to set some conversion rate goals so you know what you want that channel to deliver.
Tip: Marketing and sales channel analytics is obviously easier online than offline. Online channels are digital and often the analytics are built into the marketing and sales platforms.
Brands matter. Brand analytics seeks to determine the strength of your brand compared to your competitors. Your brand is more than just your logo and your commercial livery – it’s the look and feel of your products and what they represent to your customers. It’s important to really understand how customers perceive your brand as this will impact your decision making and strategic direction. You can source this sort of data anywhere your customers and potential customers are discussing your brand, such as customer service conversations, sales conversations, online forums, blogs, review sites, and social media.
Tip: The internet is a rich source of information regarding how people feel about your brand and your business. People love to share so tap into this rich vein of information.
- Career: Training | Books | Cheat Sheet | Apprenticeship | Certification | Salary Surveys | Jobs
- Knowledge: Research | Competitions | Webinars | Our Book | Members Only | Search DSC
- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
- What statisticians think about data scientists
- Data Science Compared to 16 Analytic Disciplines
- 10 types of data scientists
- 91 job interview questions for data scientists
- 50 Questions to Test True Data Science Knowledge
- 24 Uses of Statistical Modeling
- 21 data science systems used by Amazon to operate its business
- Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 22 tips for better data science
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- High versus low-level data science
Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge