Good Decision Making Requires Good Data
If you begin with bad data, you won't make the best decision.
Over the years, I have become a "data hound" looking for every morsel of wisdom I can ge to help me make smarter decisions. The good news here: accurate data is king. You can't effectively manage your business without accurate data. Getting it is not always easy but without it you risk making the wrong business decisions -- hurting your business when you thought you were helping it. Allow me to explain.
Managing a sales pipeline.
In B2B businesses with long sales cycle the only way to assess the effectiveness of your sales team and predict future revenue is based on data your sales team enters into your CRM. Watch if a salesperson's number of accounts is growing, how those leads are working their way through the sales funnel and total dollar value of the pipeline being managed.
But, think about what I just said: you are evaluating the success of your sales team based on the data they are entering (or not entering) in the CRM system. That creates multiple problems. I have seen situations where salespeople enter false information to look more successful to save their jobs. More generally, there is plenty of room for error any time you rely on humans for data.
For example, did the salesperson remember to enter a new lead into the CRM? Did they remember to update the status of a lead (e.g., from active to dead)? Did they update the dollar value of that lead from $20,000 to $10,000 after they learned the client didn't need as many products as they first thought? Did they update the expected close date from April to June, after they learned the project has been delayed?
You get the point. Most businesses are making mission critical decisions based on future expected revenues from this data. More often than not, the data is not very accurate, updated or reliable.
If your CRM suggests you are working with more than $1,000,000 of potential leads, and your normal conversion rate is 20 percent, you would think there is a reasonable chance to close $200,000 in sales. That's money you count on to run the business, pay your bills and meet payroll. Bad data could put you in an illiquid position, unless you have a cash reserve cover the $200,000 that didn't show up as predicted.
You need to scrub the data when managing a sales pipelines. Every week, remind your salespeople to update their data. In your one-on-one meetings with the team, talk through their list, line by line, to ensure what the system data is telling you is reality. Where you can, build automated systems that update data for any actions made (e.g., as new email leads come into business, they automatically get entered in CRM). This includes building in automated tasks and reminders to make sure the leads are moving forward and the salespeople are getting system-triggered actions they need to take for each lead.
Most good CRMs or sales enablement tools can help you here.
Managing marketing spend.
The quality of your marketing efforts depends on the quality of the data being managed and studied. Typically, there are two problems. First, is your marketing team managing towards the right data metrics in the first place. Second, is credit being given to the marketing channel that actually drove the lead? In a multi-device world it's not easy to get proper attribution.
Recently we hired an ad agency to manage our paid search campaign. We told them the key metric to drive was immediate return on ad spend (ROAS), defined as clearly attributed revenues from the campaign divided by marketing cost of campaign. A strange thing started to happen in our business: our low ticket, online ecommerce transactions started to take off, but our desired high ticket, offline B2B transactions were not growing at all. By telling our agency to focus on "immediate" ROAS, the only way they could hit the desired target was by focusing on smaller orders that were immediately ready to book online. That excluded the desired longer sales cycle leads we really wanted to be growing.
So, after six months of these learnings, we switched directions. We told the agency immediate ROAS was no longer the goal. We would be happy waiting until the end of our three-month sales cycle before studying our ROAS. We switched the key metric to immediate B2B leads from the marketing effort. As soon as we made that change our quick, low ticket sales fell back to normal levels but our desired B2B leads rose to record highs. We were thrilled, thinking we had finally "cracked the code" to scaling our business.
But, did we? We did a retroactive cohort analysis of all B2B leads that came into the business over our normal three month booking window. What we learned was concerning: the B2B leads were coming into the business in record numbers, but were converting into sales at levels far lower than our typical conversion rates. After researching this further with our sales team we learned the leads coming in were very price sensitive. They were shopping many websites for the lowest price and often needed last-minute deliveries that were impossible to fulfill in time.
So, now we are back to the drawing board, trying to figure out the right metric to find leads we can actually work with and properly attribute the leads so we are not missing anything important. We also want to be careful not to "throw the baby out with the bath water". Maybe the marketing agency is actually doing a great job and something operational is getting in the way of sales converting. Time will tell.
These are examples in sales and marketing but I easily could have given you data-driven examples from operations, finance, human resources or technology. You are living in a world where accurate data is king. Be sure your business is driven by the metrics that are the most important and reliable for predicting and driving desired outcomes. The data is only as good as the effort you put into it.
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