From random numbers to actionable intel: how to manage your big data

Written by Kelsey Jones, Product Marketing Manager at Salesforce

Data has many benefits. But with data points constantly being collected and updated, marketers can have trouble managing it. Data is only useful when it’s actionable.

When it comes to big data management, actionable goals that give a purpose to your data will make it more manageable and useful. Here are some of the areas where you can put your big data to work and use it to guide your future campaigns and strategy.

Purchasing Behaviour

The purchasing behavior of your customers may be your biggest data asset. By analyzing specific metrics of how and why customers make purchases, you can see what works and what doesn’t in your marketing strategy. Some metrics that are important to look at in your data include:

  • Conversion Rate: Your average conversion rate can help determine whether or not new campaigns or products are successful. If you try a new product page and see a higher conversion rate, then you know the product page is converting better than the previous version.
  • Industry Conversion Rate: When you see how your organization’s conversion rate compares to your industry average, you can determine if your campaigns are as successful as they should be.
  • Repeat Customer Rate: How many times does an average customer make a purchase from you? This can help determine your sales funnel and repeat customer targeting strategy.
  • Average Order Amount: If the average order amount is less than the cost of some of your premium products, you may see more revenue from promoting more of your lower-cost products. Try experimenting with different selling methods (for example, BOGO, bundles).

Amount Spent on Site Before Purchasing: This relates to the buying cycle. If the average customer visits your product pages three times before making a purchase, they may need more convincing on their first visit. You need to test product page conversion tweaks to try to convince them to make a purchase sooner.

With some big data solutions, you can compare your results to industry standards — such as the industry conversion rate. Use your own data combined with a mixture of data from other sources to make more accurate decisions.

Continue Building with Demographics and Behaviours

Knowing the basics about your average customer helps you form personas, create new campaigns, and build better products. In a process known as data mining, look at your data for patterns in gender, age, location, and interests to flesh out your answer to the question “Who is my average customer?”

Combine these patterns with your metrics and other common customer behaviors. For instance, if you know that your customer base skews 30- to 45-year-old adults who make an average purchase of $100 on your site and reach out to customer service using the phone, you can spend more time testing call tracking and evaluate whether there needs to be more phone support for customers.

Combined data will give you the most information available. This helps all departments make better decisions, including evaluating employee performance, tracking trends, and predicting cash flow.

Campaign Integration

Big data management is all about combining the data you have in the most robust system available so your employees have the information they need.

As you create a picture of who your average customer is, you can integrate other data to better target the behavior patterns of those most likely to make a purchase from you. For instance, you can use social media analytics to pull more data that is relevant to behavioral targeting: It helps you see how and where your customers are engaging with your brand on social media. This will help with asset allocation when it comes time to decide advertising budgets or employee time management. It will also help you see which existing efforts are actually turning into conversions.

If you use Facebook pixel tracking, you can see what percentages of sales come or originate from Facebook. If a customer clicks on an ad about your new product suite and doesn’t complete the lead form, but follows up later to fill out a free content offer, the pixel gives you data on all the customer’s previous actions before completing the lead generation form.

Having better, integrated data allows you to see if all your marketing efforts are worthwhile. For instance, if your company has been creating quarterly white papers, but the data shows that white papers only make up 2% of your total lead source, you can experiment with different facets of the white papers (for example, the length, topic, or landing page copy) or consider cutting them from your marketing plan and refocusing those resources on something that generates a higher percentage of leads.

Sales Integration

Because marketing is mainly responsible for and focused on its own efforts, it may not see where a lead goes once it is collected for the company. This is why customer data from multiple departments and sources should be integrated into one big data management strategy.

In addition, leveraging third-party data sources can help companies predict what products customers in a specific industry will like. This can help shape both marketing and sales strategy and outreach. By using the right platform to collect all external and internal data into one place, you can see from which source a customer originates (for example, social media) as well as their average sales cycle and location. A centralized source of your data is beneficial to all departments.

For instance, if your digital marketing department sees that the average sales cycle of a customer is 12 weeks, but its campaigns usually only run for six weeks, it needs to look into lengthening campaigns to capture more leads within the normal sales cycle.

Integrating data into one platform helps you make better predictions, according to Doug Camplejohn in an article for CIOReview. By analyzing lots of data simultaneously, you can predict sales, customer rate of return, and several other metrics that can influence sales and marketing goals.

Goal Setting

Knowing key data points helps a great deal with goal setting. With the 12-week campaign example, the digital marketing department knows it needs quarterly goals that correspond with its average sales cycle.
Predictive analytics is another tool for big data that helps teams set more effective goals. Looking at a mix of internal and external data can let you determine any outliers. If you had a quarter that doesn’t go as well as predicted, for instance, but historically that’s unusual, you can set clearer goals based off the historical data and focus on what caused the quarter to go the way it did.

Looking at past performance trends can create clearer expectations, which makes goals more attainable. If the past three marketing campaigns have a strong start and then see incoming leads drop by 25% after two weeks, you can compare this historical performance to current campaign performance to see if you hit the mark.

Better use of data is all about leveraging it the right way to make more informed decisions. Big data management tools can help companies organise their own data, and use data from third parties to help predict behaviours and learn purchasing patterns. By using all the available knowledge, organisations can make better decisions for both marketing and overall product strategy.

If you want to learn more about data and predictive analysis check out: It’s All About Data: Understanding Predictive Analytics

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This article was originally published here and was reposted with permission.

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