Big data. Data science. Machine learning. Artificial intelligence. Buzz words. We have all heard them before, but what do they really mean and how do we implement them into our brand strategy? Get ready … here comes your crash course!
Data collection is ALWAYS happening. Every time you click “like,” close out of that full shopping cart on Amazon, and even when you mistakenly grab 1% milk instead of 2%, data is being collected. There is SO MUCH data. To make sense of all the data, and to help us draw meaningful conclusions from it, we have to analyze it.
We’re not talking about the dystopian cubicle farm at Google where millions of people track your every move, and NO, Facebook isn’t listening to you. Data analysts take all the numbers and interactions that surround your business in a day and turn it into action.
Analytics is the act of using data to make strategic decisions. When the quantity of data becomes too large or complex to analyze in traditional ways (e.g. Microsoft Excel), it’s referred to as big data. Data science involves the application of using computers (machines) to apply analytical models to big data sets (learning) in order to glean insight. This level of advanced processing of data by computers is also referred to as artificial intelligence.
Studies have shown that when companies adopt data-driven marketing strategies, they see as much as 30 percent in cost efficiency savings and 20 percent in revenue. Umm…YES, PLEASE! If you’ve ever wondered how to achieve it or simply where to start, rest assured, we’re here to help.
We’re such big believers in using insights to drive marketing decisions, we’ve baked it into our name. And although we may brag a lot about the creative “Fire” aspect of MindFire, the real juice is in the “Mind.” Our Brand Arsonists use analytical models to dive into our clients’ data and mine for hidden gems that drive powerful brand and marketing strategies. We’re going to let you behind the curtain with these five steps to boost your brand with analytics.
As the saying goes, garbage in, garbage out. This is especially true in analytics. The quality of insights from your data is fully dependent upon the quality and quantity of data you collect. Now, the key here is not to collect as much data as you can, but to collect enough of the right data you’ll actually use.
Beyond the basics of collecting contact information (e.g., address, email, phone) tied to transactions, key demographic (e.g., industry/customer type, gender, age, income range) and psychographic (e.g., values, hobbies) data can prove extremely useful for analytic insight. Keep in mind that what doesn’t make sense to gather from your customer sign-up or online forms can be collected via customer surveys. And to take it one step further, we’d be hitting the jackpot if we could connect your user experience data (e.g., click-rates, web visits, etc.) directly to your customer database.
All businesses do some form of data collection. Some of the most common platforms for pulling marketing analytics include Google Analytics, marketing automation software programs and social media analytics (Facebook, Instagram, Twitter, LinkedIn, etc.).
The more you truly know about what your customers think and how they act, the more relevant and effective your marketing efforts will be. With the right data in hand, you can begin to understand your customers and their purchase behaviors on a whole new level.
Begin by looking for overall trends in your data. Then, dive deeper to look for trends by product offering, geography, demographics and psychographics.
Identifying the similarities and differences between customers, their purchases and how they interact with your brand is the perfect jumping off point in determining who your target audience is and how to target them. It leads to identifying customer segments, persona development and customized messaging unique to each segment – because what resonates with one might not be as effective for another. Ultimately, this magnitude of customer insight will help guide your decisions and prioritize your marketing resources based on the lucrativeness of your customer segments.
Online shopping is a prime example of enhanced customer personalization fueled by data. I’m talking about the “frequently bought with” or “you may also like” scrollable sections at the bottom of pages. These are intelligent models that push the most likely purchase, not only for you as a user, but according to that company’s internal data. Let’s look at a few examples.
Recently, our client New Pioneer Food Co-op, a Midwestern grocery chain, reached out to define complementary products to suggest to their customers. By applying a machine learning algorithmic model to their existing point-of-sale purchase data, we were able to find commonly paired purchases and predict the likelihood a particular product would be purchased based on what’s already in the shopper’s cart. This information provides direction for in-store product placement, targeted promotions, automated ecommerce product recommendations and enhanced in store and online customer experiences.
Or let’s look at Carhartt, a work apparel company, for a larger scale example. The company uses the IP addresses of their website visitors to alter the products and messaging shown based on the weather in their location. Web visitors in warmer climates see t-shirts on the homepage while those in cooler climates see coats. CRAZY RIGHT? It’s happening! #ThanksData
Let’s talk about efficient budget allocation. Optimization models, which is a fancy way of saying math equations, are giving businesses the ability to make their dollars go further. These models take the guessing game out of where to allocate resources by determining the optimal allocation that will meet your business requirements. They can be used to minimize cost, maximize profit or achieve the best possible quality.
So, the next time you’re about to launch a multi-channel marketing campaign and need to decide how much of your budget to spend on each channel to achieve the audience reach required, remember that analytics can provide the direction you need. With this confidence, marketing teams are able to best utilize funds to produce more effective campaigns.
Before you embark on any marketing tactic, it’s important to first determine what success will look like. Based on your marketing strategy, are you aiming to increase brand awareness, customer engagement levels, conversation rates? By clearly defining success first, you’ll know exactly what key performance indicators (KPIs) to track, which is a giant step in making analytics work for you and ensuring you’re actually moving the needle.
Our secret? We don’t wait until a campaign ends to review its metrics. The minute the campaign begins, we monitor the numbers closely. Keeping a pulse on the numbers allows you to turn on a dime and reallocate your resources to the areas where you are getting the highest results. Hello ROI!
Also, remember to make KPI analysis a regular part of your routine. Which email subject lines, headlines, ads, images…fill in the blank…saw the greatest success? Keep track of what works and what doesn’t work – because that goes back to truly knowing your audience.
Trusting your gut every step of the way is risky. Analytics takes the guesswork out of the process and puts the facts right in front of you. And if the idea of pouring over your own data sounds daunting, our Arsonists have the tools to take your endless data files and turn those spreadsheets into dollars.
Knowing how your existing data and perhaps newly obtained data can be used to further enhance your brand is just an email away.