In today’s digital business landscape, the reality is that customers talk about brands, and brands listen in on many different digital platforms. One of your goals as a digital strategist is to create a brand environment that invites customer and brand interactions, and through these interactions collect demographic and preference information. With this customer information, you can better understand who they are and what they are looking for.

An effective approach for collecting your customer information is by setting up a demand generating (rich media) digital environment, such as a webcast, video, or podcast; another good digital option is a website resource center. While there is a lot of attention placed on the popular big three social platforms, such as Facebook, LinkedIn and Twitter, rich media strategies can provide you with more engagement control and customer information (data), for sales leads, email campaign and digital marketing strategy

A company resource center gives your customers an opportunity to research your product or service, and as important, provides you with an opportunity to capture user information. This information can be converted into sales leads (where customers are in the buying cycle). Your resource center should include not only white papers but more importantly embedded rich media and events.

Digital Strategy:

In the digital world, customer demographic and preference information typically comes from email correspondences, social media, web site interactions, and transactions. This data can be collected by using forms, which provide the customer with a digital space to put their personal (preference) and business (demographic) information.

Now, this is where good data management skills come into play, why, because the data you upload to your marketing automation system or CRM should be error-free; the ease in which data can be pulled out from its database system, will also affect your campaign strategies effectiveness. As a digital strategist, you should have an understanding of how to use automated forms (sometimes called short forms) as part of your strategy, and also have a data plan.

A data-plan requires information to be standardized, which ensures that customer information is entered correctly; this process is called normalizing your data. Your automated data forms, where customers input their demographic and preference information, will also encourage customers to provide valuable intelligence information and enter it correctly.

Data entry forms can be effective in capturing anywhere from 16 to 20 fields of information from a customer. Generally speaking, a barometer for how much information customers will provide, depends on how many questions they initially complete. If they answer 7 or 8 questions (demographic and/or preference), there is a good chance they will answer a total of 10 to 15 additional ones. Remember, receiving sufficient demographic and preference information is critical to the next stage of your collection process; knowing if your leads are strong or weak (warm or cold). This information will also help you achieve a greater return (sales conversion rate) on your leads.

Normalizing your customer data (writing everything the same way) before it is uploaded (by CSV or API method), into your marketing or CRM system will keep dirty data that is not labeled properly, from entering your automation system. Allowing only properly labeled data will make it much easier to pull out the right customer information when you are ready to create leads for a digital campaign or sales team.

Ultimately you will be looking for lead information that provides sufficient demographic and/or preference customer data about where the buyer is in the buying process. This information translates into warm or cold sales leads. Warm leads are preferable because they are an indication that a particular customer is more interested in your product or service.

For example, proper normalization can prevent the following error from happening: August 10, 2019, and 8/10/2019 are the same date to Americans, but the following can mean October 10, 2019, if it is transferred to a database in some European countries. Or even something as obvious as one automation system collecting customer location information using an abbreviation for a state (NY instead of New York), could cause problems in identifying the customer and potential sales lead for a campaign or lead request. Why, because it may cause you to perform two or more database runs, and additional analysis to examine content with various state spelling types.

To fix this problem, you would want to normalize your data, although before you do that you would have to determine if the added man-hours it takes to correct it is within your campaign budget. On the other hand, not being able to use your customer data could cause you to lose some potentially valuable contacts for a campaign or sales lead strategy.

That being said, when data collection is automated, computer output is usually normalized. For data that is compiled and entered by humans, you should speak with your data input team regarding proper data-entry rules, so you can maintain good normalization standards.

Once you have your customer data uploaded into your marketing or CRM automation system, you will want to decide which customers are more relevant for your campaign or sales team to contact. There is an effective and efficient way to ensure that some leads are more valuable than others, and that can be accomplished by rating and ranking your leads, known as lead scoring.

This is the point when the analysis of your data takes on real value because lead scoring can improve sales conversion success rates. While It does take a systematic approach to work with your customer information, in the end, research shows that in enterprise business environments, lead scoring can increase a company’s annual revenues by approximately 45 percent, lead generation by 25 percent, and reduce cost per lead by approximately 20 percent.

With well-executed lead scoring, you can do a better job at managing your leads, find users, and bring them to your website or resource center, which will then generate additional information through an auto-response back from your digital content.

An example of some widely used lead scoring models are high demand ones that emphasize user preferences (80 percent behavioral and 20 percent demographics), balanced (50 percent demographics and 50 percent preferences), and low activity demographics (80 percent demographics and 20 percent preferences), the last model is normally used by companies that are looking to quickly reach as many new customers as possible (generally new companies), and may not be overly concerned about selectivity that preference information provides.

Worth noting is that human preference information has been found to be more effective for sales purposes than demographics alone. Why, because demographics can change, while preferences tend not to. Research has shown, that self-reporting conversions rates are 10 times greater, 1.0 percent from self-reporting versus 0.1 percent for demographics.

The lead scoring process should also be a collaborative process, with business units such as sales, customer service, and marketing involved. Your scoring objectives are to evaluate data, map traits or habits of online users, understand conversion and threshold points, and determine the best case elements of a sales-ready lead. Once you are accustomed to using this process, you will find that your customer data through scoring will lead to increased profits, better leads, and added productivity.

Finally, over the past several years a lot of attention has been placed on social media, engagement, and listening, with less on data management. Engagement through digital marketing agency can be an exciting and motivating process, while data management is thought of like a less interesting technical task, left to a database administrator (DBA) and analytics specialist.

You can grow brand through Pinterest too, even many businesses and bloggers having huge success by get relevant followers on Pinterest.

This is a misconception because normalization techniques and lead scoring are deeply rooted in a digital strategist’s ability to be successful; to launch an effective lead generation or digital campaign that does more than engage and look great. As we move forward in the digital strategy process, we will increase our use of internet-based social technologies for business and communication purposes, and conversion rates and ROI expectations will increase.

That is why becoming more responsible and knowledgeable about the entire digital process (the funnel), through good digital strategies, and practices, including using data strategically is important. At the end of the day, a company’s success depends on its sales team’s ability to make a large number of deals in less time.

While I am not suggesting that you take on the DBA’s and analytics responsibilities, but that you have a good understanding of why data management and analysis are important and how to implement its practices. It will also provide you with an additional opportunity to engage with your customers, create a feedback loop based on demographics and preferences. You will also be more successful as a strategist moving products or services. Having more knowledge about your customers can also extend the life of your customer relationship.

By adding a systematic approach to managing your digital data, such as using rich media demand generating strategies, data collection, normalization, and lead scoring, you will experience greater success rates with your digital strategies and digital properties. Why, because you will know where to find your customers, how to engage with them, and how to give them what they want, so they come back. These processes should be part of the digital strategists planning process because they are rooted in the digital engagement process.