Image Credits: hobsonassoc.com
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In the past few years, we have seen scientists trying to come up with forced conclusions on the basis of analysis of data from a given sample. And the results are as much poles apart as they can be. The world of startups and sales in startups is no different from this geeky world of scientists. Gone are the days when sales team consisted of extrovert talkers who talked people out of anything and into anything. The sales team now consists of business intelligence or data experts who read signals from various sales data and suggest how to sell.
But when super genius scientists are not perfect in their analysis, how much do we trust those data crunchers working on a limited set of data, which is the case of most startups? Well, the answer is, we don’t. Ideally, a startup should take data driven decisions, especially in sales, but keeping in mind the quirkiness of the decisions. And they should try to always work on a bigger sample of data which they do not have until they involve some external research.
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So, here are a few conclusions which can be helpful in sales and which are the results of some number games done by a nerdy data analyst. It tries to simplify results deliberately.
- Let’s see this graph
So what can we read from the above graph? We can see that people using Credit Cards spend the most in the month of October and spend the least in the month of February. In fact, spend of February 2015 is lower than the spend of October 2014.
Can this be used to our advantage in Sales? We can have one simple conclusion for sure- do not ignore the importance of the festive months of September and October, whichever domain your company is in. Be it Diwali or other regional festivals, this is the time when salaried people in India get bonuses and this is time when businesses get a boost. Another conclusion is the dullness which February brings due to less number of days and due to the fear of appraisal in the minds of salaried employees which force them to spend less.
- Now what if we add another data set to the above set of data, to understand more about the sales/purchase psychology?
This graph in itself may not be very interesting, but when combined with the above point, shows us that fashion plays a major role in credit card spend. And it is a higher tendency to use credit card for luxury than for necessities. Which further corroborates the point mentioned in 1 about luxury spending increasing in October and luxury spending seeing a dip in February.
- Now lets see another version of graph in Point 1.
This version gives the same information as mentioned in Point 1, when seen in isolation, that October sees a jump and February sees a dip. But let’s put the numbers in a simple program, and see if the combination of data in points 1, 2 and 3 can give some more conclusion. Well, it does!
We see that in October, not only does the overall sales increase, but average value of transaction also increases, and most transactions happen in the luxury segment. So, more people buy more things per person in October. The rich increase the size of their purchase, while the not-so-rich wait for the month to come to do some purchase.
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We also see something interesting happening in June. June sees no increase or decrease in the number of transactions, but sees a significant decrease in the size of transaction, and a significant decrease in the purchase of luxury items. Is it because of the onset of monsoons or the fact that marriage season has just become over and luxury items are less in demand? Well, we will need more data points to conclude on June. But it does give insights useful for sales team.
Conclusion: In short, data can be used to make many conclusions. The above was just a layman example of showing some power of data. If a startup has a sales team, and if the sales team does not have a person who can read and understand data, the startup is still living in the old ages when sales did not mean what it means today!
(Disclaimer: This is a guest post submitted on Techstory by the mentioned authors.All the contents and images in the article have been provided to Techstory by the authors of the article. Techstory is not responsible or liable for any content in this article.)
About The Author:
An avid reader and writer of tech and finance, he can beat you at Table Tennis any day !