It would be an understatement to say that sales and marketing have changed since the dawn of the digital age. They’ve been turned on their heads. It’s hard to even fathom that only a few decades ago, door-to-door sales and cold calls reigned. Oh, how times have changed.
According to Gartner, by 2020, at least 85 percent of the relationships between customers and companies will be completed without any human interaction. Not only have customer journeys become much more complex, but consumers are in the driver’s seat now, steering their own experiences with brands. They expect those brands to fully understand where they are on the road and what they need to make an informed purchasing decision.
Business leaders have begun to recognize that big data and analytics are having a major impact on sales and marketing, and there’s no avoiding the change. Enterprises that aren’t successfully integrating their marketing tools and data are falling behind. Companies need to match specific products with particular buyers at the perfect time to have any chance of competing in a digitally-transformed landscape.
But not to worry — machine learning-driven tools now exist to easily collect, analyze, integrate, and act upon big data. Steve Cole, Senior Principal at Maven Wave, demonstrated and discussed Maven Wave’s take on those solutions in his “Right Message, Right Product, Right Time: Game Changing Marketing with GCP and Machine Learning” talk at Google Cloud Next ’18.
“As a former CMO, I wish I had this a few years ago,” Mr. Cole said. “I think it is really game-changing for the clients that we’re working for.”
Maven Wave uses Google BigQuery as an aggregation point to create marketing data warehouses for clients. The platform analyzes and visualizes company data in easy-to-understand dashboards, making recommendations for the most strategic next steps, based on aggregated info about the customer, their journey, and the marketing department’s goals.
Mr. Cole demonstrated how this would work with a fictitious automotive manufacturer, called Salient. BigQuery gives the marketing manager specific recommendations about how to sell certain vehicles that have been on the lot for longer than the ideal time frame of 60 days. Based on user preferences, the platform then implements those suggested changes, either automatically or after manual review and approval. After determining that the manufacturer is outperforming its competitors in the TV category, but underperforming on keywords, BigQuery moves some of the dealership’s regional incentive plan budget from TV advertising to a specific keyword in Google AdWords. The platform also recommends that the marketing manager deploy incentives on a certain vehicle.
Mr. Cole’s example shows how BigQuery helps sales and marketing departments build a warehouse of data, visualize and analyze that data, and then implement effective changes to support the findings.
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