What’s the difference between ‘Big Data’ and ‘Data’?
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.
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:
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.
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.
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.
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.
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
This article was originally published here and was reposted with permission.
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