Taking Aim: How to Identify Your Target Audience

What's your mark?

Your business – and every business – has a singular driving goal: capturing more customers and, ultimately, owning your market. Today, owning your market doesn’t require you to spend more on marketing and promoting your business than your competitors. However, it does require you to have a more efficient marketing and promotion engine in place that yields maximum return for every dollar and every hour you invest. The process of building this engine doesn’t begin with tactics; rather, it begins with identifying your target. No business – yours included – can afford to market to everyone. Of course, it’s tempting to try to reach as broad an audience as possible in the hopes that no potential customer will slip through your fingers. However, in actuality, this approach will cost you much more money and deliver far less satisfactory results because you’ll have greatly diluted your chances of reaching those whose needs you truly serve. It’s important to understand that your target audience does not encompass anyone who might possibly ever buy your products or services. Rather, your target audience is comprised of those who are most likely to buy and, therefore, become the primary focus of your marketing efforts. Identifying the right niche provides the foundation for success in all aspects of marketing and promoting your business:

• It allows you to focus your efforts on those tactics and mediums that are most effective in reaching this particular group.
• It allows you to tailor your sales message to focus on the needs and concerns that are of greatest relevancy and urgency.
• Most importantly in today’s marketplace, it allows you to build a strong community around your brand comprised of people who love what you do and happily serve as your fans and evangelists.
• And in real dollars and cents, it’s the difference between sending thousands and hundreds of thousands of postcards to achieve the same end result.

Here are the steps you should take to ensure that you’re targeting the right audience:

Segment your customer base.

Who are your customers? Of course, there are many different ways to answer this question. Often it’s easiest to begin by examining your sales data and segmenting your customers into groups based on demographic factors, including age, gender, income level, education level, marital and family status, industry and geographic location. In working though this process, you’ll likely find that a particular group or groups emerge as those who buy from you most often. This simple step can also help you identify how best to market the same product or service to different groups. Some market segments may be better reached at trade shows while others can be reached at home with a direct mail campaign.

Dig deeper.

Breaking your customer base down into groups based on basic common characteristics like gender and income is only step one. To reach and engage with these groups effectively, you’ll need to develop a deeper understanding of both their lifestyle and their motivations. Start with one of your products or services and evaluate it through the eyes of the customers that exist within each group you’ve identified. Make a list that includes every possible reason this type of customer might want this particular product or service. Maybe they are trying to solve a problem, maybe they just want to feel good about themselves or to satisfy a basic need. Going through this process will help you drill down to the specific benefits and outcomes that should be the core focus of all your future communication with this group. Secondly, think about the routines of their day-to-day lives and how this communication will be best received. Do they frequently read the paper? Do they spend a lot of time in the car listening to the radio? When searching for news and entertainment, do they turn on the television or pick up their iPad? Are they likely to be active on social media platforms and, if so, which ones? This type of analysis is essential to ensuring that you choose the right vehicles and mediums to capture their attention.

Keep digging.

At this point, you’ve established a solid foundation of knowledge about your target audience. But if you dig a little deeper, you might uncover additional information that will allow you to sharpen your approach even more. Now that you’ve segmented your market and gained an understanding of what drives your customers, see if you can identify which group or groups offer the most marketing bang for your buck. For example, of all those who are most likely to buy your products or services, which groups represent the most profitable? In the B2B world, these are usually the clients with the greatest longevity or those who utilize services with the greatest profit margin. Also, who are the customers or clients that send you the most referrals? These are your very best customers because they do the work of selling for you, so make sure you are not only reaching your existing customers who fall into this category but also others like them because they represent a group whose needs you are particularly good at serving. Finally, it would be a mistake not to examine who your competitors are targeting. This is not so that you can just copy their strategy and run with it. To the contrary, what you’re really looking for is any gaps in the market that they might be overlooking so you can swoop in and grab these underserved segments.

Put your target to the test.

Now that you’ve identified your audience, it’s time for the rubber of your marketing plan to hit the road of execution, right? Not so fast. You need to put your construct of your target audience to the test to ensure that it’s one that can sustain and grow your business. This process is often called a SWOTT (strengths, weaknesses, opportunity, threats and trends) analysis. Ask yourself the following questions:

• Are there enough people within the audience you’ve identified to support your business?
• Can they afford your product?
• Will they see a legitimate need for it?
• Where do your prices fall in regard to their expectations? Too low? Too high?
• Are there opportunities to upsell other related products or services to this group?
• How much competition already exists in the marketplace for this group?
• Are there any trends you can identify of which you should be taking advantage?

While the process of identifying your target audience may seem complex, these steps hold the keys to competing effectively in today’s marketplace. When you clearly understand who buys from you and why, only then can you find the channels they frequent and become one with your tribe. And wouldn’t you rather own your target market than merely shoot arrows into the dark, hoping one will land?

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Intelligent Design: Transform Your Website into a Sales Engine with Machine Learning

Machine learning: The phrase evokes images of computers playing chess or IBM’s Watson destroying two legendary Jeopardy! champions in a three-day tournament. The truth is, though, machine learning is no longer a novelty; it’s now an integral part of our daily lives. Every time you receive a product recommendation from Amazon, your email server weeds out spam before it reaches your inbox or you enjoy a playlist on Pandora, you’re seeing machine learning in action. In a nutshell, machine learning is the science of training computers to recognize data patterns and make adjustments automatically when those patterns change. While on the surface this may not sound very exciting, nothing could be further from the truth. In fact, machine learning is the key to transforming your website into a lean, mean selling machine.

Understanding machine learning in 100 words or less

Machine learning uses algorithms to build models from data; as more data is collected, the algorithms are “trained” to adapt to changes. There are two ways in which machine learning can be implemented: supervised and unsupervised. Supervised learning algorithms are used to create models that establish relationships between types of data — the relationship between purchase data and user clickstream data, for example. Unsupervised learning uses algorithms to gain insights into customer behaviors and preferences by looking for patterns within the data. Both of these methodologies are designed to make marketing and e-commerce more exact, more personal and more profitable.

Putting machine learning to work

Netflix, Pandora and Amazon are all familiar examples of machine learning in action. All three use recommender systems powered by complex algorithms. These systems collect data about your browsing activities, past selections and any ratings or reviews you may have provided. Then they segment you into clusters with other customers who have demonstrated similar interests or behaviors and use this data to suggest items that might appeal to you based on the browsing and purchasing habits of these other customers. You see this on Netflix as the category titled “Because you watched...” and on Amazon as “Customers who viewed this also viewed...” To gain a deeper understanding of how these algorithms work, let’s take a closer look at Amazon. To Amazon, you are a very long row of numbers in a massive table of data. Your row represents everything you’ve looked at, clicked on, purchased (or, equally as important, not purchased) or reviewed on the site. The other rows in this gargantuan table encompass the same thing for the millions of other customers who shop on Amazon. With every click, visit and purchase, more data is added to your row, which allows Amazon to constantly mold and shape the products it recommends to you and the special offers you receive based on an ever-evolving stream of information about you that is being collected and stored. Another innovative example is True Fit, a retail software start-up that is on a mission to apply data analytics to increase customer confidence in online clothing purchases while decreasing the number of returns for e-railers. Well-known fashion retailers, including Nordstrom, Macy’s and Guess, have implemented True Fit’s algorithms on their e-commerce sites. When customers shop on these sites, they’re asked to create a profile that includes their height, weight and perhaps most importantly, the size and brand of their favorite piece of clothing. Using that data, True Fit is able to recommend the correct size for a specific brand and article of clothing. Even more importantly, as customers continue to use the True Fit system, it learns more about their personal style and preferences and steers them toward purchases they’ll be more likely to keep and enjoy rather than return.

How machine learning drives smarter marketing

You don’t need the resources of major e-commerce giants like Amazon or Netflix to take advantage of machine learning to to improve your e-commerce site and your online marketing efforts. By enhancing your existing site with systems that allow you to create a virtual marketing intelligence brain, you can create a more personalized – and therefore higher quality – shopping experience for your customers. By establishing this type of marketing intelligence ecosystem, you can mine the data provided by customers every time they visit your site to answer vital questions that will help you fine-tune your site and your online marketing strategy – questions like these:

• How likely is a given website visitor to convert?
• What behaviors characterize customers who are likely to buy?
• What behaviors characterize customers who are likely not to buy?
• How can new visitors be identified as high-potential long-term customers?
• Which type of web traffic has the most value?
• Which products or services appeal most to a given segment of customers?
• Given the contents of a particular customer’s shopping cart, which additional products are high-potential recommendations?
• How can website visits be optimized to provide the best possible experience for each individual customer?

Making it personal

The final question in the list above is one that deserves special notice because of the staggering potential for using machine learning to create a more personalized shopping experience – one of the key drivers for increasing online sales. Not only can the data collected via such marketing intelligence ecosystems be used to drive recommender systems, it can also be used to create personalized advertising based on market segments — or even individual profiles — that can be distributed across a variety of desktop, mobile and social platforms. This type of advertising can be tailored to any number of personal preferences and demographic information, including age, marital status, location, lifestyle choices, typical purchases, brand preferences and so on. Ads can be focused to such a granular level that they reflect specific colors a given customer prefers, and their individual purchase drivers, such as status or cost-effectiveness. Another exciting aspect of machine learning-based personalization is the development of individual customer profiles. You can even combine online and offline customer data to create a more complete picture of a given user. Types of data included in this profile might include online and in-store purchases, membership and activity in rewards programs, product ratings and clothing sizes. Just imagine how much more powerful your marketing efforts could be if you were armed with this level of information. One of the most important aspects of a successful marketing intelligence ecosystem is how data mined from customer activities is combined with sound business rules in order to make smart recommendations that are well received by customers and that do not compromise their trust in your brand. For example, most people who walk into a supermarket like bananas and will often buy some. So shouldn’t the recommender simply recommend bananas to every customer? No – because it wouldn’t help the customer, and it wouldn’t increase banana sales. So a smart supermarket recommender would always include a rule to exclude recommending bananas. At the other end of the spectrum, the recommender shouldn’t push high-margin items just because it’s beneficial to the seller’s bottom line. It’s like going to a restaurant where the server steers you toward a particular high-dollar entree. Is it really his favorite? Or did the chef urge the staff to push the dish because it comes with a side order of premium mark-up? To build trust, the best recommender systems strive for some degree of transparency by giving customers clues as to why a particular item was recommended and letting them adjust their profile if they don’t like the recommendations they’re receiving.

Science fact, not fiction

Machine learning can give your business a serious competitive edge by opening the door new opportunities in the marketplace. It can help you personalize and improve your customer experience dramatically and thereby drive sales and revenues. Creatives and developers alike are rapidly pioneering new and innovative ways for marketers to use machine learning — and the future of marketing built on these ideas has seemingly endless possibilities.

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