5 steps to starting analytics for your business

Many people don’t understand how marketing can lead a business, which is why analytics is vital to justifying it, said senior marketer for Polycom, Sue Day, at Marketing’s Analytics Interactive conference.

Start with the basics and build your analytics over time, advised Day.

For Polycom, marketing is B2B, and the focus is always demand/pipeline generation. “Even our PR department is always talking about driving leads.”

Day lists five steps in starting analytics for businesses, referring back to her experience in starting Polycom’s:

Step 1: Research the right tools

Day talks about choosing the right research tools amid the myriad of choices available. For Polycom, it uses Salesforce as its lead management tool, and it needed its other systems to integrate with it. It also uses Eloqua for its marketing automation.

Step 2: Implement consistent practices

After setting the right tools in place, the brand came to a point where organising consistent practices became important. “We needed to think about the processes around keeping leads from sources such as its events, survey forms, inbound queries, web forms, etc,” she said.

“And speed is of the essence when it comes to competition,” said Day, talking about the need to integrate this quickly to get an edge.

Step 3: Document process

The IT function is critical to document process, said Day. This involves alignment between functions and process and data management between systems.

Step 4: Training and communications

Next, training and communications is key, particularly for marketing and telemarketing activities. Training staff to make the most of these processes is vital.

Step 5: Analytics

The last step is where analytics comes in. “You must decide what you want to measure. Until the system is totally in place it is hard to decide what you can gain from it.”

Quoting Peter Drucker – “what gets measured, gets managed” – Day said it was important to focus and decide what one needs to measure the most.

“The key is not to produce data for the sake of it – but ensure it is useful and usable.”

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