Elasticiti solves scale challenges by automating sales forecasting for several large networks
Required forecasting skills the team didn’t have and Excel wasn’t designed for
Leverage the power of historical data in data warehouse
R for data munging
Tableau to visualize booking curves on per-brand basis
The heads of Finance and Sales of these major broadcasters wanted to better forecast revenue for its directly sold digital ad campaigns in order to answer key business questions:
How does revenue pace, and are the sales teams on target to achieve revenue goals?
How does that revenue pacing differ by quarter and client?
Is there a way to identify potential problems in time to address them?
To answer these questions, these clients wanted an easy and more accurate way to understand how revenue accumulates on the books, and to compare revenue YoY to assess whether the teams are performing better, worse, or as expected to plan.
Advertising and traffic patterns aren’t always in alignment. Sometimes, for reasons that have little to do with the business, traffic patterns can be high, but selling low, or vice versa. For instance this can occur in the summer when consumers have more time to spend on the Web, but the people who are directly responsible for placing and processing advertising I/Os are away on vacation. Other variances may be caused by macro trends, such as a downturn in the economy that makes advertisers jittery.
These organizations wanted to examine their historical data, or track records, so they could identify recognizable patterns in how revenue collects on their books. Put another way, the client wanted to plot booking curves for revenue.
Going deeper, individual brands, such as the sports or entertainment sections of the site, experience their own booking curves, and understanding those variances would enable the sales team to proactively spot issues that need addressing.
Challenge: Messy painful process
Extremely Difficult to Extract and Normalize Historical Data. At its heart, plotting a booking curve is a data science exercise, which means that all data inputs need to be standard. But the historical data had significant variations in categories over time. There was no authoritative naming of categories or time period.
Historical Analysis in Excel Workbooks. All existing historical analyses were built in Excel workbooks, which meant extracting that data was far from straightforward. And the analysts continuously evolved their analysis, which meant there were a lot of natural variations within these Excel workbooks.
Booking Curve Complexities. A booking curve is the intersection of the capabilities of the sales staff, combined with the buying habits of each client. A site with numerous categories -- general news, entertainment, sports, health, etc. -- will have a unique booking curve per section, making them a complex exercise in data science.
Data Disparity. A big challenge was the disparity of data. To get a full picture of the revenue, the head of sales needed to tap into a wide range of data sources, including its Salesforce.com and Operative systems. But sales professionals are experts in the dynamics of the market, not data science.
Data Capture and Data Warehouse Issues. Because this was a new initiative, the data wasn’t stored in a way that would facilitate this type of exercise. The clients needed to change the way date was stored, but lacked the skills or time to do so. The project required fluency in more robust databases such as Teradata, Snowflake, as well as BI tools, such Tableau, Microstrategy or Looker.
Real-Time Data Difficult to Achieve. Although the teams used spreadsheets to gain some insights, the process wasn’t scalable, and required significant manual input. That meant it was difficult to get insights on demand updated in real time.
No Future or Historical Views. The spreadsheets didn’t allow a level of drilling, either in future or historical views, that the sales team needed to track current performance on the booking curve.
The clients engaged Elasticiti to develop a model as quickly as possible. The team began creating an authoritative way to represent categories and time periods. Next it pulled and normalized the historical data from the Excel workbooks, Salesforce.com and Operative, and imported it into R, which is well suited for normalizing disparate datasets, as well as crunching data.
Creating the booking curves was more of a data science exercise. Using linear regression, Elasticiti plotted how sales accumulated on the books over time. This data exercise revealed that each quarter begins with some portion of its available inventory already sold. As the year progresses, the percentage of pre-sold inventory increases, so that by Q4, up to 90% of the revenue the network would receive for the quarter was already booked. Looking back over several years, Elasticiti was able to build annual and quarterly booking curves -- insight that has implications for both revenue and inventory forecasting.
“Booking curves are highly complex,” explained Ben Reid, CEO of Elasticiti. “The curves change as you look across sales teams, properties, and time periods. Building up the model was just one aspect. We also needed to smooth out agreed upon anomalies or variations in the signal, but still retain general pattern. That’s where the art of data analysis came in.”
The team also built numerous features into the model, such as what-if scenarios to help the head of sales to answer questions about the impact of potential market events on revenue, and alerts that trigger if revenue isn’t tracking as anticipated.
To make it easy for the head of sales to spot trends quickly and easily, Elasticiti fed the data into Tableau, which created user-friendly dashboards, available from any PC or laptop. The dashboards are continuously updated in real time, so accurate information is always available.
Results more timely and accurate, every step of the way
Timely and Accurate Insights. The heads of Sales now have more timely and accurate insights into sales efforts at every step along the way. The booking curves eliminate any surprises by making it easy to see how sales are trending compared to prior quarters.
Far More Accurate Forecasting of Revenue Results. With the booking curves in place, sales management is able to forecast revenue results with much higher degrees of accuracy, and with a lot less effort on their part.
Early Warning. The solution provides an early warning signal for the heads of sales and finance teams, that earnings may not be what they anticipated. This signal allows them to take corrective actions, and advise the appropriate C-level executives.
What-If Scenarios. The heads of sales can easily test what-if scenarios and predict their implications on revenue.
More Time to Focus on Sales. The dashboards have eliminated the countless hours spent entering data into spreadsheets, which means sales executives have more time to focus on strategic sales initiatives.
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