It was a pleasant Monday morning when I…
Who am I kidding? No Monday mornings are pleasant. Anyways, before I digress further, it was one Monday morning that I came into the office. I saw my VP – Growth, Mark fidgeting with his MacBook.
Throughout his career, Mark has stayed true to his creed of sales. He is a seasoned sales leader with over 14 years clocked in closing deals, retaining customers, and more.
In short, Mark was, is, and will always be a true blue salesman.
After exchanging obvious pleasantries, I settled in my chair and powered up my laptop.
The notification of my to-do list popped up. As with any content marketer, I let out a sigh. There it was. The deadline for the blog – “The Role of Self-Service Analytics in a Data-Driven World” was today. I cursed myself for staying in late but missing even one FIFA world cup match (even if they were group matches) was out of the question.
My sigh was pretty loud enough that I heard a faint voice behind me.
“What happened man?”
I turned around to see Mark standing there. I explained my predicament. He replied, “Is that it? I’m not sure if I would be able to help you structure this into a B2B blog, you’re the expert. But I can share a few instances with you. That might help you get some points.”
And that’s what this is going to be about. This is not my story, this is Mark’s and his lifelong affinity toward data.
Case I – If not for Spreadsheets…
This case starts with his career stint at one of the leading organizations in the leisure and hospitality sector. He had a team of young sales professionals reporting to him. Now as with any sales leader he had hard targets which trickled down to the team.
The pressure was there but the team always outperformed. Mark was happy, the management was happy, and the customers were happy.
But one fine day, he noticed that there was a small anomaly in the figures. There were a lot of delinquencies in realizing the full revenue. This was a model where payment will happen upfront using a credit card, and then the customers pay a monthly EMI using a credit card. The credit card amount takes some time to be disbursed after verifying card details as it was a manual process. I would like to bring to the attention of the readers that this was the time when credit card fraud was not detected using technology. Every month when the EMI was processed, at least 50% of the credit card transactions were declined. Yet, these credit card numbers were sent for EMI processing every month despite the repeated declines.
Mark pulled up the customer sign-up details in a spreadsheet. Then he started with the tough task of analyzing data, manually.
- He created a pivot table
- He compared the declines across various payment instruments like cheques and credit cards
- He plotted a trend to analyze how long this anomaly existed
He did more to finally arrive at the conclusion that a few salespeople in the team were rigging the system. They were using a retail loyalty card instead of a credit card (to meet the targets) and by the time the results came back as payment declined to the finance team, the prospective customer would already have enjoyed his/her holiday benefits and the company was losing revenue realization opportunities.
It took more than 90 business days to effectively analyze, derive insights, and take an action. But the damage was already done, suffice it to say the revenue loss was substantial.
Spreadsheets are the original self-service Gods but they exact payment of blood in return!
- Time Delays in terms of extracting the info, cleaning the data, and arriving at a conclusion
- Spreadsheets were complex to master and Mark had to spend more than 1000 Man Hours to gain speed
- They were not compatible with Big Data and the data was spread across multiple sheets creating silos
Case II – It’s the Era of Data Analysts & Colorful Dashboards
The second case was an intriguing one. This time Mark found himself with a leading Fast Moving Consumer Goods brand. He was tasked with increasing the profit margin for their deodorant and body soap business lines. The numbers were larger when compared to the hospitality industry, which also assured additional pressure. But the brand was a very well-known name amongst the target audience so the targets were easier to achieve. Add to this, very understanding retail shop owners. All was well. For a while at least.
Then the issue crept without anyone noticing it. But Mark never misses anything. Even though the sales figures were being achieved, it looked like for Zone A, the deodorant business was outperforming body soap by a large margin. Knowing the consumer behavior in that sector, this phenomenon shouldn’t happen. The entire zonal sales team was confused as to pinpoint the change in figures.
Mark reached out to his data analysts team and first collected the previous three quarters’ performance results of the two business lines. The market share was almost equal, with either the deodorant or the body soap leading by a narrow margin alternatively. Then he requested a dashboard from the data analyst team which should contain
- Retailer’s margin for soaps vs deodorants
- Retailer’s margin for his soap brand vs top 3 competitor’s soap brands
- Retail shelf space for his soap brand vs top 3 competitor’s soap brands
- The positioning and key messaging of the top 3 competitor’s soap brands
- Price point comparison for his soap brand vs top 3 competitor’s soap brands
- No. of SKUs ordered by retailers for his soap brand vs top 3 competitor’s soap brands
- Soap sales in terms of size – 3 Ounces vs 2 Ounces
The data analysts started with the process. But the time to get the data, cleanse it, and present the dashboard itself took 10 days. On top of that, it was a static dashboard which required iterations to present all the information in tandem. The required reworks took another 10 days. The final version came to Mark on the 20th day because the data analysts were clueless about the importance of the metrics and how they will impact market share. The 20-day delay impacted the strategy as consumer behavior again shifted and other competitors got a better market share. This back and forth continued and reaching the target for body soap sales turned into a catch-up game which took its own sweet time.
Depending on Data Analysts is Better but Comes with a Cost
- Bureaucratic processes with strict timelines of delivery cripple many business strategies at the stage of inception
- Communication gaps between two teams lead to rework on the same dashboard or report multiple times
- Data analysts are stuck in a transactional work mode and their true potential remains untapped thereby diminishing value creation
Case III – Self-Service? More like Self-Liberating
The third case with Mark happens when he was working in the media industry. The flamboyant media sector with all its glamour is ruthless when it comes to ad sales. There will be cutthroat competition amongst all the big media houses for air time from prospective buyers. 99% of the time it turns out to be a price war with the one who is outbidding his competitor winning the deal.
There was this deal with a prospective designer clothes boutique firm. At the time of closing the sale, Mark seldom knew this deal was happening with the devil. Because the contract signed in its fine print spoke about a clause where the air time cost per second would remain the same for the prime time throughout the year for screening their ads 20 times a day. To nullify the contract, double the amount of free air time needed to be given to the customer.
The sales team targets will increase substantially during seasonal times like Christmas, Thanksgiving, Halloween, etc. because the demand for ads goes up and along with it cost per air time. Mark also had the same targets. But even after closing more deals, he wasn’t able to achieve the target. This repeated for two major seasonal periods. Before Christmas comes Mark needs to find the revenue leak and plug it.
Mark reached out to his data analytics team and got the dataset in the requested format. Technology had come a long way with self-service analytics tools helping business users directly generate dashboards and reports in a specific format. Mark fed the dataset into the data analytics tool and downloaded all the required reports in just 4 business days.
- He ensured that he ranked his customers in terms of their spending in descending order
- He plotted the spending trend for
- Air time purchases
- Number of times ads were screened
- Customers ads that were screened during prime time
- Customers whose ads were screened only during seasonal time
Then the devil appeared. It seemed like the boutique firm only screened their ads during seasonal times for a lower cost during prime time. This turned out to be a roadblock in gaining higher revenues.
After doing quick calculations, Mark nullified the contract by giving the customers free air time between 12 PM – 2 PM, the non-prime time with the lowest viewership because the clause only spoke about free air time, and the timeframe was not specified. Double whammy for the client!
The Role of Self-Service Analytics in Mark’s story was Simple
- Ensured that the insights were received with lesser delays as compared to others
- Reduced the dependency on external teams except to get the dataset
- Reworks did not happen as Mark was the user and he had clarity on the comparisons required
But, Self-Service Analytics Needs to Get Sharper
- Even though compared to spreadsheets, the results were faster, 4 business days is still a large delay in the current business scenario
- Technical complexity has gone up significantly in the past decade, and these tools require specific certifications
- Self Service Dashboards work on historical data and is reactive with no real-time insights, whereas today’s world needs proactive actions
- Mark is a highly trained data user but a fresh user will be overwhelmed by these self-service analytics tools
Mark says “Right now, I wanna Talk to my Data!”
Mark concluded his three cases and asked me, “So! Did I give you something to work on?”. I was already paying rapt attention without bothering about the blog and dismissed that question with a wave of my hand.
Then I popped my question, “Now what? We’re already saying that Self-Service Analytics is not enough”.
Mark smiled and told me “It’s always enough, just that there needs to be a small change. Remove the technological bottlenecks.
- Ensure that every user irrespective of his skillset and station in the organization should be able to access insights
- The accessed insights should answer the user’s momentary data answers instead of overloading them with information
- The interface that they’re interacting with to derive insights should be as intuitive as it can be”
I was interested. We’re discussing the next frontier in Business Intelligence. I went on “Do we have anything on that angle?”
Mark was like “Yes we do – Conversational Insights. The benefits outweigh the cons of existing BI tools in many ways. You should actively read more about it. “
Mark continued, “It works with the language as an interface, the most intuitive and the simplest of all interfaces where humans do not need any active training.”
“If you ask me what I want right now? I wanna Talk to my Data and derive insights now. Not months later, weeks later, or even days later. Because time waits for none, why should I wait for my insights? It costs me money, opportunity, and more!”
“This is where we are working towards making a difference. By the way, if you would like to see conversational insights working on real-world datasets, you better tune into last week’s webinar.”
I nodded my head. And opened my browser. I headed over to YouTube and typed these magical words in the search bar. The words were
Maybe you should watch the webinar too. Who knows? It may change the way you look at insights forever.