The makers of the popular mobile app Shazam has opted for Salesforce's Einstein Analytics as it bids to modernise its reporting processes and move away from a reliance on Microsoft Excel spreadsheets.
Shazam Entertainment is a London-based company known best for its popular consumer mobile app, which can recognise and identify a song simply by listening to a short clip.
The eighteen-year-old company has recently transitioned its business model towards advertising revenue through Shazam for Brands, reporting in September that it had hit EBITDA profitability and was seeing double-digit revenue growth. It previously relied on affiliate revenue by referring customers to streaming services like Spotify and Apple Music.
Speaking to Computerworld UK, vice president of advertising operations at Shazam Pete Miles said that the company was pretty much reliant on Excel spreadsheets for internal reporting and managing sales to advertisers -- pull data from Salesforce CRM into Excel, create and distribute pivot table -- when it came to getting sales pipeline reports and forecasts in front of sales managers.
"Looking at data over time was problematic, we always though it was a snapshot," he explained. "Now that advertising is the majority of revenue it became apparent that we needed to deal with this problem of getting the insights we needed and to reach data earlier."
In terms of off-the-shelf, software-as-a-service (SaaS) solutions, Einstein Analytics stood out for Miles as it was native to Salesforce, which meant a simple integration with their existing CRM, and frankly, because it was cost effective.
Shazam started the deployment in July 2016 and was able to go live by January 2017, led by a single employee and requiring no third party consultants. It was initially rolled out to just nine key members of staff.
Miles admits that the initial data cleansing process prior to the integration was a large undertaking, but now the company is in the best possible position to leverage that data. "It forced us to get our house in order, to improve the quality of the input," he said. "The real value comes in a virtuous circle, as they start to value the data and then see the importance of keeping the inputs clean."
Why Einstein Analytics?
The branding of analytics products at Salesforce is wildly confusing, especially considering that Einstein Analytics is a rebrand of Wave Analytics.
There is sales cloud or service cloud analytics for forecasting and basic pipeline KPIs, which costs $75 (£58) per user per month. Then there is Einstein Analytics, which allows for more data analysis, custom dashboards and apps, and is priced on a bespoke basis.
Miles says that the initial investment for Einstein Analytics has been "light" and the additional cost per head has been low. Shazam claims that "the deployment provides an average annual benefit of $246,896 due to increased productivity and time saved", for key staff.
"While there are potentially better solutions in terms of capability and visualisation, for the price point and the problem we wanted to solve, it was really the only choice," Miles added.
Despite the use of the Einstein brand, Einstein Analytics alone isn't particularly 'smart'. The machine learning powered functionality is branded as Einstein Discovery, which promises to surface insights and make predictions and recommendations using historic data, automatically within the platform. This is an add on for Einstein Analytics and is charged on a bespoke basis.
Miles admits that Shazam isn't using the predictive capabilities of the platform yet, and wishes that some of the more predictive capabilities were bundled into the core platform.
"From a selfish point of view it would be nice to see some of the AI capabilities being bundled in off-the-shelf, but I understand lots of that is yet another add on," he said. "Optimum times to reach out and probability of close are interesting features, whether we will start using any of those we aren't really sure yet."
Instead, Shazam has been happy to be able to access data on-demand, rather than waiting for analysts to send out weekly reports. The end users are still analytics specialists, but now they can focus on bespoke reporting while sales leadership can self-serve weekly reports and check in on their pipeline on-demand.