The 3 Key Steps to Optimize your Content Marketing Strategy with White Papers

content marketing strategy

You have in your possession a well-researched, highly-engaging white paper – now what? White papers have no innate benefit in your overall content marketing strategy if you do not promote and leverage them properly. When it comes to successfully using your white paper to generate leads and secure sales, there are three necessary steps you should follow:

Step 1: Create a Proper Landing Page

More often than not, a company’s white papers are deeply hidden within their website. To even reach the white paper, potential buyers are forced to navigate through several web pages. Once buyers find the proper white paper landing page, it is often lack-luster: failing to highlight the benefits and merits of downloading and reading the paper.

To maximize your content marketing strategy, it is important to place your white paper on its very own dedicated page. This landing page should inform the reader of the benefits gained from reading your white paper. Providing these benefits can assist in convincing potential buyers that downloading and reading your insights is worth their time and energy.

To write an impactful and effective white paper landing page, it is helpful to include the following elements:

  • Keywords – To draw traffic to your website, it is well-known that SEO optimization is key. This optimization starts by selecting the keyword of your company’s choice, and ensuring that it is included multiple times on your website’s various pages. Once you have selected your relevant keyword, a rough guide for your white paper landing page is to aim for a keyword density of between 2-7%.
  • Minimal text – The biggest bulk of text on your white paper landing page should be the introduction paragraph. This paragraph should include a catchy and grabbing headline to draw the reader in and a sentence or two of what your white paper is about.
  • Emphasize benefits – All other text on your landing page, excluding your introduction paragraph, should be bulleted. Bullets provide readers essential information in a quick, streamlined way. These bullets should focus on emphasizing the “what” of your white paper – what are the benefits of downloading and reading this paper? If you cannot convince visitors to your landing page that they will be better off having read your white paper, they will not bother to download it.

 

Step 2: Collect Information

When it comes to granting access to your white paper, you have two options: a direct link or gating your content with a form. The smartest option for boosting your content marketing strategy is to offer your white paper in exchange for personal information.

Asking your visitors for information allows you to generate valuable leads. By gathering the names and e-mail addresses of your visitors, you can more easily convince them to opt-into your marketing efforts – your future emails, newsletters, and blog posts. Gaining your visitors information and establishing a relationship with them through sharing your marketing content is more likely to lead to successfully incorporating them into your sales funnel.

It is tempting to ask those wishing to download your white paper for as much personal information as possible – resist the urge. By asking visitors for too much information up-front, you risk alienating them. First time visitors to your company’s website have no personal connection with your company, so they are not likely to want to spend too much time filling out forms.

To increase the likelihood of collecting visitor’s information and connecting with them in the future, keep it simple – ask for their name, company, and e-mail address.

 

Step 3: Post on Social Media

As of 2018, there are over 3 billion active social media users worldwide – that’s nearly 40% of the global population. With such a large, easily accessible pool of potential leads, you would be remiss to not include social media in your white paper’s content marketing strategy.

Social media marketing is vastly important in gaining visibility and driving traffic to your website. When it comes to white papers, 90% of marketers claim that white paper social media marketing has generated immense exposure for their company [i]. By posting engaging, informative content on social media, businesses are more likely to see click-throughs to their website. This increased visibility leads to greater leads generated and conversions – over 66% of marketers see lead generation benefits with social media usage [ii].

The best approach to not only promote downloads of your white paper, but to attract attention to your business as a whole is through the adoption of social media into your content marketing strategy.

 

To learn more about how white papers can transform your content marketing strategy click here.

Big Data is transforming the Food and Beverage Industry

food and beverage and big data

A 2015 McKinsey study reported that food retailers can improve their operating margins by up to 60% simply by harnessing the power of Big Data. In order to keep pace with consumers’ fickle buying habits, food and beverage companies need to begin combining raw point-of-sale data with the Big Data that is now available. Analytical capabilities then can transform this data into meaningful intelligence that can inform management decisions. Those decisions will boost sales and improve their overall bottom-line performance. For example, food and beverage retailers, suppliers, and trading partners can share Big Data to ensure they offer the right products, in the right quantities, in individual stores and online.

Big Data Helps Drive In-Store Revenues

Food and beverage companies can use Big Data to increase traffic to their brick and mortar stores. The GPS location capabilities of most mobile phones provide a channel for retailers to display “pop-up” promotional messages that are highly relevant to an individual’s specific location and past purchasing history. A shopper, for example, standing in a frozen food aisle can receive a text offering a discount for a certain ice cream flavor nearby that she has bought in the past.

Big Data Helps Schedule Food Deliveries

Big Data can optimize on-time deliveries of orders to restaurants, food chains, and home customers. Big Data will collect recent information from various sources about road traffic, weather, temperature, routes, etc. and provide an accurate estimate of the orders’ times. This data analysis helps ensure that food/beverage companies don’t waste their resources transporting stale products. They will deliver perishable food items when they are fresh.

Big Data Helps Allocate Food Across the Country

By using Big Data to track purchasing decisions from wholesalers down to the customer level, food and beverage companies can learn what products are being purchased and where. For example, a company might learn that customers in the Pacific Northwest are purchasing 15% more of a diet beverage than the nationwide average. Further, they may learn that the Midwest is purchasing 15% less of that same beverage. This knowledge allows the company to know to ship more of the diet product to the Pacific Northwest and less to the Midwest.

Big Data Helps Maintain Consistent Food Quality

Big Data allows restaurants to maintain consistent quality of their products. Consumers expect the same taste in food at the chain restaurants they love. The taste of food not only depends upon the proper measurement of ingredients, but also on their quality, storage, and season. Big Data analytics can analyze such changes and predict the impact of each on the food quality and taste. The insights from these analyses will be used to identify pain points and suggest measures for improvement.

Big Data Analyzes Customer Sentiment

Big Data can analyze customer sentiment by monitoring customer emotions expressed on social media networks. Food companies use sentiment analysis to track their customers’ emotions. They can assess negative reviews and take appropriate preventive steps before the word spreads. Large food retailers like McDonald’s, KFC, and Pizza Hut have found this particularly valuable.

Big Data Has a Good Idea What Customers Will Purchase Next

Food and beverage companies use Big Data for “market basket analysis.” Market basket analysis is a technique which predicts the most obvious item that a customer is likely to purchase next based on her purchase history and the items already in her cart. Food retailers and restaurants use these projections to create effective combo deals and improve their marketing messages. For example, if the market basket analysis identifies that a customer prefers a muffin with her coffee, then it can create a combo to help her enjoy them together.

Big Data in the Home Improvement Industry

home-improvement-big-data

The Home Depot is the unchallenged leader in the home improvement retail sector in terms of applying Big Data to advantage. The Home Depot collects data from its own website, promotional emails, and social media. It uses that information to drive traffic to its stores by improving their marketing programs. As a result, The Home Depot is beating investors’ expectations and is described as “Amazon-proof.” Interestingly, this is happening at the same time many retailers are struggling to connect with their customers and deliver meaningful results to their investors.

The Home Depot Will Spend $4 Billion Over Three Years on Big Data

The Home Depot is spending roughly $4 billion from 2016-2018 to improve the company’s e-commerce platform and physical stores and bolster the link between the two. The Home Depot is creating a system that allows customers to easily order what they need online, have their employees collect these orders in store, and let their customers drop by the stores to pick up their purchases in moments. This buy-online, pickup in-store (BOPIS) model has proven vital for The Home Depot. According to the 2016 Internet Retailer’s report, about 25% of The Home Depot’s $3.76 billion in total website sales, or nearly $1 billion, came exclusively from their BOPIS program.

The Home Depot Uses Big Data to Reconfigure Its Supply Chain

Using technology and Big Data to rethink The Home Depot’s supply chain has been a key part of the company’s success. It will prove increasingly vital as the company moves forward. The Home Depot has used Big Data to improve its supply chain several ways:

  • Dynamic ETA, which gives customers delivery data and delivery estimates based on their exact location.
  • Sync is a multi-year project that will reduce shipping and inventory costs through better coordination between stores and distribution centers.
  • Its Customer Order Management System helps balance store and web inventories. It also enables buy-online, pickup in-store customers to choose the store with the shortest wait time for pick up rather than requiring customers to choose stores only by location.
  • An easy-to-use website and mobile shopping platform will make the customer experience more seamless while allowing The Home Depot to better collect customer data. It will use that data to further improve its Big Data initiatives

Wayfair Is a Winner Due to Big Data

Home goods e-commerce company Wayfair was created in the digital ecosystem of 2002. Since then it has thrived due to its consistent commitment to and use of Big Data. In 2016, Wayfair introduced a search with photo capability. This capability taps into Wayfair’s Computer Vision System. This Vision System is based on the company’s own machine learning techniques and its massive proprietary data sets. This system allows customers to upload images of furniture they are looking for and Wayfair will give customers search results that match the image provided as closely as possible.

This data collected from this visual search feature creates a powerful feedback loop, which makes Wayfair’s results more useful for customers. Wayfair measures the impact of its photo search system by tracking the number of loyal repeat customers. In the second quarter of 2017, the numbers of orders per customer and repeat customers both increased year-over-year. The number of repeat customers grew to be more than 61% of total orders in the second quarter of 2017; this compares well to the 58% in the second quarter of 2016. Repeat customers placed 2.6 million orders in the second quarter of 2017, an increase of 55% year-over-year. These increases in repeat customers and their orders is testament to the effectiveness of the Big Data driven photo search capability.

How Medical Mobile Apps are Transforming Healthcare

mobile medical apps

Medical mobile apps are transforming the Healthcare Industry, promising to improve quality of healthcare while lowering costs.

In 2017, global medical healthcare apps were a $26 billion industry with a global average CAGR of 32.5%. The United States currently has the largest market for mobile medical apps. However, the Asia-Pacific region is showing the fastest growth rate in the world – with an estimated average CAGR of 70.8%. By 2022, the worldwide mobile medical app market is anticipated to reach a $102.43 billion.

As of 2017, mobile healthcare apps have been downloaded over 3.2 billion times – this marks a 25% increase since 2015. In the United States alone, there are over 500 million smartphone users with mobile health-related apps. The greatest growth in mobile medical apps has been in the management of chronic care – particularly diabetes, obesity, high blood pressure, cancer, and cardiac illnesses.

As the prevalence of chronic illnesses worldwide increases, so is the increase in medical apps created to help manage these chronic illnesses. Nearly half of all Americans, around 133 million individuals, currently live with a chronic illness. Per the Centers for Disease Control and Prevention, now seven of the top ten causes of death in the US are due either directly or partially to chronic illness.

Chronic illness is on the rise globally as well. According to the World Health Organization, as of 2017, over 79% of all deaths related to chronic illness occur in developing countries, and this rate is anticipated to continue to climb. Heart diseases and other cardiovascular illnesses will continue to be the major cause of mortality throughout the globe. Asia, in particular, is experiencing the greatest rise is cardiac disease and death due to heart-related complications.

The widespread availability of tablets and smartphones in healthcare today is what is helping spur the use of mobile healthcare apps by patients and providers alike. According to referralmd, over 80% of physicians in 2017 use their smartphone at the point of care – whether for patient services or for administrative reasons. The wide access to and use of smartphones by providers and patients alike has been the primary driver behind the increasing availability of mobile healthcare apps year-over-year.

How can mobile apps help? What kind of mobile apps do patients want? And which kind do physicians need?

The healthcare industry is filled with opportunities for digitally savvy companies and mobile app developers.

Download and read the full article here.

Big Data is transforming the Auto Industry

Big Data in Auto Industry

The next few years are going to see an explosion in the rate at which detailed data is collected about the moment-by-moment operation of nearly all new cars. This data will be stored and collated in centralized databases that make Big Data analyses possible. McKinsey published a report in 2014 that estimated that the global market for connectivity components and services for cars was $38 billion that year. The report went on to project that the data-driven connected car industry would grow to $215 billion.

Big Data Will Transform Fleet Management

Fleet management will enjoy the greatest benefit from this Big Data analysis. Auto makers will now be able to determine which settings and features drivers actually use. This will help them improve their marketing. It will also identify the features that drivers really care about; this will focus the auto makers’ on-going R&D efforts.

Further, automakers can easily monitor their cars, identify potential problems, and issue maintenance calls. This will help maintain their fleets in peak performance. They will be able to identify drivers who are abusing their cars; they can issue advisories based on that information. All of these efforts are geared to minimizing the maintenance costs and maximizing the performance of their fleets.

Big Data Is Transforming At least Five Other Auto Practices

City Planning — City planners and engineers can use this same data to improve their plans for roadways and traffic flows.

Onboard Navigation — Navigation systems can use real time driving data to discover and display the fastest routes based on current traffic patterns.

Insurance Rates — Insurance companies can access the Big Data collected from connected cars to monitor each driver’s performance and, potentially, use this information to adjust rates and to determine what really happened in accidents.

Auto Dealer Marketing Campaigns — Dealers can use this Big Data to assist in planning their marketing campaigns. For example, Bullseye Prospecting is a product that helps dealers and their marketing agencies automate their marketing campaigns by leveraging third party and internal data on consumer behavior, incentives, and vehicle equity/valuation. This prospecting tool can cut the $600-$800 average per-car cost of sale by about 30%. It also helps dealers by sending a detailed, personalized message to their best customers at precisely the right time to prompt sales and services.

Used Car Valuations and Inventory Management — The 2016 Black Book survey indicated that nearly two-thirds of dealers are using 30%-50% more data since 2014 to establish vehicle valuations. This data also helps them set regional pricing, determine the appropriate supply of cars, and assess each vehicle’s history to manage their inventories. Some 69% of these dealers say the data is giving them better insights on pricing and profitability. 58% say Big Data is providing better insight into managing their inventory procurement. The majority of dealers believe they can avoid a market catastrophe similar to the one in 2008 because the data allows them to make more accurate decisions.