Financial Institutions Can Put Artificial Intelligence to Much Better Use

I’ll start with a potentially controversial statement. Banks are misallocating their investment in artificial intelligence and predictive analytics by putting it into consumer-facing chatbots, rather than using it internally to empower their staff to understand and better serve the customer.

I’ll start with a potentially controversial statement. Banks are misallocating their investment in artificial intelligence and predictive analytics by putting it into consumer-facing chatbots, rather than using it internally to empower their staff to understand and better serve the customer.

Most customers don’t like speaking with bots and usually call their bank when they have an issue that requires processing that’s beyond what artificial intelligence can currently offer. In fact, AI’s reputation has been damaged virtually beyond recovery by the endless loop most customers encounter when they call the bank, not able to get to where they want to go.

Moreover, you don’t see pictures of chatbots pinned up in banks with “Employee of the Month” emblazoned across the bottom. Nor was any new business won on the strength of a chatbot’s performance. Finally, customers don’t stay with banks because they developed a great working relationship with a chatbot. Truth of the matter, chat hasn’t reached the level where it’s consistently reliable for addressing the customer concerns that rise to the level of making a call to a financial institution.

All that said, artificial intelligence is a highly powerful tool. How it’s being used is simply being misallocated. So the question becomes, is there a way banks can use it to enhance human engagement with clients? The answer is, “Yes.” Although banks and other financial institutions are in a completely different line of business than, say, a luxury retailer or car dealership, what they have in common is that critical need to engage customers at various points in a given transaction. This applies to banks and other financial institutions at least as much as it applies to other businesses. Reaching out to, connecting with and maintaining relationships with customers, and doing it well, is a key consideration. Done well, banks have a better chance of securing a higher lifetime value from their clients when they get it right. And it’s much harder for bankers or advisers to know about the hundreds of products that are available to them; far more so than, say, a car salesman at a dealership, or an associate in the dress department at Saks. AI’s best use is providing them — the customer-facing bank advisers — with the tools to have the right information for the right client, so they can spend more time on the customer relationship.

There are ways in which the power of predictive analytics can be brought to bear immediately, creating a more substantial and recognizable benefit for both financial services providers and their customers. A knowledge-driven approach to cross-selling and upselling is one such strategy.

There’s a vast range of training, tools and processes that can positively influence engagement efforts. But predictive analytics can push these initiatives into a much higher gear, providing a uniquely powerful impact when it comes to solidifying those all-important bonds with customers. Through better analysis and use of data that’s already available to most financial institutions in petabytes, it’s possible to learn more about customers, and consequently offer them more relevant service, support and product options. The right, internal approach to applying predictive analytics, therefore, results in benefits for both customers and the financial services providers they work with — a true win-win situation.

Historically, banks — especially large ones — tend to lean more toward conservative, careful approaches to new strategies and technology than quick movement and adoption. Given the mound of compliance mandates that govern their every engagement, this is understandable. But it but can be a significant drawback. This is where predictive analytics can sharpen their game. Many institutions have demonstrated a resistance to adopting this specific tool, or have used it in a very limited way. But they’re missing out on the benefits. And understanding the inherent pitfalls in predictive analytics is key to achieving success in deploying it.

How Financial Institutions Can Effectively Deploy Predictive Analytics

It’s a given that cross-selling and upselling help create more lifetime value from customers. But finding strong connections between products and clients is still a complicated process; particularly when you have to juggle moving parts, such as customer credit scores, income, credit utilization, and the like. Figuring out what products you can sell to whom, and predicting what those outcomes will be, constitutes a successful cross-sell. When done correctly and ethically, cross-selling can ultimately strengthen the customer relationship into a lifetime value — read, profitability — for the bank. This is because they’re able to match a product that was needed with a demand that they’ve identified.

It’s 20/20 hindsight, but we all know about the debacle of Wells Fargo’s unethical cross-selling and upselling, and how much trouble it got into as a result. With upselling, predictive analytics can really make a difference in the campaign to upsell. And unlike the Wells Fargo situation, this approach is sustainable. Looking through vast amounts of consumer data can help banks to understand how relationships have historically evolved between the bank and its consumer over time. On the consumer side, the spotlight is on how their data is being used. Only by robust analysis of customer behavior — ideally where multiple products are being offered — can banks regain their customers’ trust that their data is being used to benefit them.

Predictive analytics platforms can conduct this type of analysis, leaning on demographic information, as well as purchasing and financial data that institutions already have from past customer activity. All in real-time. Such an analysis would be prohibitive in terms of time, were trained experts to do the crunching. The predictive analytics tool can then offer sharply defined, personalized, relevant recommendations for staff members to share, while they continue to provide the critical human element in the cross-selling and upselling processes.

Where does this data come from? The sheer volume of payments data that banks gather, whether credit card, utilities, rent or many more — can inform what financial product the customer might be looking for and can afford, creating a sharper, more relevant offering. And that’s where artificial intelligence and predictive analytics can play a role that helps bankers sharpen their game and engage more successfully with their customers, without throwing them on the mercy of the bots. Incidentally, it also proves the notion that artificial intelligence is less about displacing humans and more about helping them perform higher-value work.

Securing profitable customers — back to the lifetime value concept — is job No. 1 for banks, whether small or large. Successfully cross-selling — truly matching a product with an identified need — goes a long way to strengthen that customer relationship. The current financial services landscape is ripe for improvement through the use of predictive analytics. Many institutions are already using advanced analytics, tied to marketing and basic interactions — but few have developed strong processes that focus on understanding customer habits and preferences. From there, they can use predictive tools to become more relevant, valuable — and humanly available — to their clients. The institutions that manage to do so will have an advantage in building stronger, longer-lasting relationships and will enjoy the increased value that comes from them.

With thanks to Carol Sabransky, SVP of Business Development, AArete, who made substantial and insightful contributions to this article.

For Transportation, Data-Driven Marketing Isn’t an Option, It’s a Requirement

There is a clear marketing advantage derived from improved efforts related to data and analytics in transportation. On-time shipments and quick, effective resolutions to issues like breakdowns and delays have a major impact on how your customers view your business and serve as a differentiator when they compare you to your competitors.

It’s now a given that companies that want to maintain and grow their position within their industry need to understand, apply and develop a deep understanding of data-driven marketing. This is especially true in the transportation industry — our focus in this article — where a variety of factors have delayed the implementation of data analysis and its application to marketing.

Currently the transportation industry is in its most favorable position ever, and is benefiting many businesses within or associated with it. Robust economic conditions, coupled with a massive driver shortage, have led to a severe capacity crunch that has enabled price increases, more selectivity with loads and destinations, and a significant amount of control in the hands of providers — a far cry from the state of the industry 18 months ago.

However, while it’s important to reap the benefits of the current state of the market, change is inevitable. Businesses face a critical need to adjust to the realities that will, sooner rather than later, transform the transportation industry — and addressing a backward state in data acquisition and analysis should be at the top of the agenda.

Addressing the Industry-Specific Barriers That Hinder Data-Driven Marketing

In the transportation world, the largest industry leaders haven’t made data analytics a priority, whether in marketing or many other areas of operation. When compared to other industries, transportation is in fact considered somewhat of a laggard. How can the industry address this?

In our experience working with transportation companies, it’s common to see access to data restricted to information technology staff and other levels of senior management, such as safety and compliance. This means no one else in the organization can utilize the gathered information and apply it to routine but vital marketing tasks such as recruitment and retention of drivers or improving route planning to increase efficiency and customer satisfaction. The legacy, siloed platforms most companies have in place for collecting and analyzing data fail to make it available for strategic decision making. Indeed, very few organizations are modernizing their systems sufficiently to maximize the value of data. Broader and more transparent access to data across the entire organization would lead to better, more measurable outcomes.

This lack of transparency, as well as companies’ outdated systems, make it harder for the industry as a whole to recruit top talent — especially for cutting edge tech positions such as blockchain specialists, data scientists, analysts, etc. A culture change that emphasizes, organization-wide, the value of data and its impact on marketing efforts and many other operational considerations is vital for future improvements, from implementing more effective solutions to hiring top-level talent.

Organization leaders will have to communicate this shift in attitude by explaining the operational improvements to be gained by changing systems that have long functioned adequately.

The end results will be worth the pain of change: improved marketing through more thorough access to data, more actionable insight into operations and an increased ability to recruit talent with the skills needed to help companies reach these objectives. Effective analysis reduces cost and complexity of a variety of core business functions by finding more efficient workflows and identifying opportunities and risk that may have gone unnoticed in the past.

How Data and Marketing Come Together in the Transportation Industry

Amazon’s entry into the transportation world is one of the clearest industry-specific examples of how an organization committed to using data and analytics can quickly disrupt the entire market. One of the clearest changes that stemmed from Amazon’s entry was a marked increase in customer expectations. By offering unparalleled access to delivery data, the e-commerce giant created a strong marketing improvement — a clear differentiator, since it can provide detailed delivery tracking  to customers in real-time.

There are many applications of data-driven marketing as it relates to transportation, from developing intelligence around customer sentiment and engagement information through advanced algorithms to tracking vehicles on the road to optimize routes and provide updates about load delivery timing. Here we’d like to discuss two key areas in need of improvement: shipment management and recruitment.

Marketing and operations can both benefit from effective management of shipments. Meeting  customer expectations by making sure an order is accurate and delivered to the right place and on time helps to build positive sentiment that leads to strong brand loyalty over time and repeat business, and ultimately a competitive advantage. That consistency is easily leveraged in marketing, where data can help marketers implement targeted campaigns to industry decision makers as well as potential customers who have visited their site. Beyond data, creating happy customers can further help socialize brand value  to other potential consumers through word of mouth.

As has been widely discussed, the transportation industry is facing a crisis in driver shortages. Recruitment efforts are another important example of how data-driven developments can impact performance and marketing outcomes. Companies can use marketing data in their efforts to find new drivers who display key attributes often associated with past and potentially future success. Businesses that can leverage data from past recruitment tactics and a variety of other sources have a better chance of attracting the types of drivers they need to retain, build and support the ongoing needs of the business.. Marketing to driver communities in an effective manner is especially important because it has a direct impact on recruitment, retention, utilization and revenue.

The Long-Term Benefits of a More Advanced Data and Analytics Strategy

Giving more credence to data and analytics, making changes to company culture and investing in the people and systems that boost analytical efforts can improve performance and long-term outlook. Improvement in data analytics abilities can enhance efforts to measure ROI and strategy, helping companies determine which specific actions are beneficial to overall performance and which aren’t. It aids recruiting efforts as well as contributing to improvements related to sales and revenue: As companies consider transformative changes and emphasize a commitment to improved analytics efforts, customers will recognize the value of these changes and seek to develop larger and longer term partnerships with those companies.

A reduction in customer churn is another critical consideration. Better use of data and related analytics efforts means more positive experiences, which can be marketed to new and existing clients. Market share is largely established in the transportation world, which means reducing churn and attracting customers from a competitor are top priorities.

On-time shipments and quick, effective resolutions to issues like breakdowns and delays have a major impact on customers and serve as a differentiator when they compare your business to your competitors. This is a clear marketing advantage that is derived from improved efforts related to data and analytics. Making this sort of transformational change represents a major step forward for transportation companies and will require significant effort and planning to successfully achieve. But the results of such a shift would be incredibly valuable, and would allow transportation companies to remain competitive in a rapidly changing ecosystem.

 

Investing in Artificial Intelligence: The Present and Future of Retail Marketing

Marketers have not fully absorbed the broader capabilities of artificial intelligence. While early digital developments such as chatbots have been widely adopted, there’s plenty of room for broader and more powerful uses. To be able to utilize AI to optimize operations, marketers should assess its current state as a marketing tool, then envision the potential future that lies beyond.

In our last article, we discussed the cost of marketing to the wrong customers. Here we’ll talk about how artificial intelligence (AI), among other applications, can help a retailer avoid the marketing missteps we discussed in our previous piece.

A transformative technology in the broadest sense, AI has the capacity to offer a variety of changes and improvements throughout the global economy. In the context of retail marketing specifically, it offers tremendous power, but is not yet well defined nor understood among all marketers. Many early digital developments, such as chatbots, have been widely adopted, but there is plenty of room for broader and more powerful uses. To be able to utilize AI for optimized operations, marketers need to understand its current state as a marketing tool, and the potential future benefits it offers.

The Current State of Marketing AI: Room for Development

The current marketing landscape as it pertains to artificial intelligence might best be described as a Wild West environment. There isn’t a strong consensus or any structured, long-term plans for the growth and use of the technology. However, as organizations learn how to harness its value, the possibility of positive growth is nearly infinite. Some retailers use few, if any, applications of AI related to marketing, but others have developed organization-wide efforts to understand, embrace and implement machine learning. However, many other companies fall in the spectrum between these two extremes and are unsure how to best utilize the power offered by thinking computer systems. Many factors play a role in current utilization, from the size of the business to the specific market segment in which it operates and the existing capabilities of staff.

Using Machine Learning to Improve the Customer Experience

A strong understanding of artificial intelligence helps businesses connect the wide-ranging powers of forward-thinking technology to their specific needs. One advantage to developing a deeper knowledge of AI is creating a seamless, personalized experience through the use of many streams of data related to customers and the merchandise they select.

Fashion retailer Rebecca Minkoff draws on machine learning to create a personalized, responsive and seamless experience for shoppers. The company has developed smart fitting rooms that identify merchandise brought into the dressing area. This allows customers to quickly make a variety of choices related to size, accessories and complementary selections. It also provides the store with more information about the shopper’s preferences. This approach supports in-store staff in an efficient way, allowing them to offer more relevant suggestions and learn from back-end data without requiring any additional work on the part of customers. This in turn allows the company as a whole to develop more effective customer profiles and leverage them to provide further benefits.

Advances like those developed and deployed by Rebecca Minkoff are examples of how innovative retailers using this new technology will win against any organizations that choose not to pursue new artificial intelligence possibilities. Making progressive change through new and expansive tools can make the difference between increasing relevance and continual struggle in a shrinking retail marketplace.

A Retail Business Must Also Be a Technology-Focused Organization

The customer experience increasingly hinges on how retailers implement and use technology, both to connect to potential customers and to drive digital and in-store purchases. Amazon’s machine learning capability, used to analyze internal and external purchases as well as personal and historical data related to its customers, led to Amazon Go brick-and-mortar storefronts. Though the stores do not operate solely on artificial intelligence, the technology has played a critical role in building the Amazon Go concept. Drawing on the desires of consumers and the types of products they want stocked in store, machine learning offers critically valuable information to retailers that makes new ventures relevant and successful.

In this way, artificial intelligence represents an extensive value proposition by offering insights into consumer behavior and needs across e-commerce sites and brick-and-mortar storefronts. While traditional marketing practices are still in use, they must be paired with technological insights to ensure that retail offers are reaching the right interested audience. If the offer is not targeted to the right potential customer base, marketing efforts will be lost with no revenue gain.

Cue the importance of a thorough understanding of technological capabilities, future goals and the best tools for the job. This understanding and development can lead to successful execution of marketing efforts that ensure the audience most likely to purchase is the one being reached. This in turn will lead to increased revenue streams. While for many businesses, this means hiring data analysts, engineers and similarly credentialed professionals to produce the crucial work and perspective for deeper insights on how AI can influence marketing operations, it remains to be seen if there are enough such qualified individuals in the workforce, or whether retailers will have to nurture talent from within their organizations.

The Future Of AI for Marketing

The broad applications made possible by thinking computer systems, and the right combination of opportunity and technology, can yield beneficial results in a variety of different circumstances, but certain areas are currently more thoroughly primed for positive change than others. In-store customer behavior tracking is one especially promising area. By observing in-store actions through footpath tracking, retail marketers can reposition product displays and overall store organization to optimize the customer experience so it is tailored toward products of highest interest.

In a similar manner, albeit a far more controversial application, facial recognition can have a major impact on the consumer experience. This area of machine learning can work to identify loyalty program members upon entrance and then offer them personalized service and promotions. This high level of customization is a unique experience that can set retailers apart from their competitors. However, some customers are hesitant to participate in facial recognition due to privacy concerns. This again highlights the need for marketers to deeply understand technology in relation to their customer base—to make informed decisions that create mutually beneficial interactions while ensuring they do not push away their target market. One potential approach to dealing with this challenge is to develop an opt-in program for facial recognition that gives customers a benefit and allows them to provide consent in return for storing their information in a retailer’s database.

Ultimately, marketers that have the capacity to intelligently consider, compare, utilize and improve the use of artificial intelligence in various applications will be the winners in a future space where technology will increasingly inform marketing decisions. Whether increasing basket size online or creating personalized in-store experiences, AI’s potential is boundless. Success or failure in harnessing it will ultimately determine who wins in the marketplace.

You’re Spending Too Much: Cast a Smaller Net for Bigger Returns With Personalized Marketing

Marketers have been spreading their net much too wide. Data analytics plays a critical role in achieving personalized marketing. Here’s what too-wide a marketing net looks like, and this is how to make it smaller, easier to control, mend and redeploy.

Marketers have been spreading their nets much too wide. Data analytics plays a critical role in achieving personalized marketing. Here’s what too-wide a marketing net looks like, and how to fine-tune it given the importance of an efficient approach to marketing to all businesses across the economy.

Failure to understand, utilize, review and update best practices around your personalized marketing model through data analytics can lead to issues ranging from excessive spending to a lack of interested potential customers. Companies have to be careful, deliberate and aware as they consistently fine tune their strategies for raising awareness of their brands and offerings in today’s diverse, digitally driven market.

In essence, organizations need to move away from a large marketing net that is likely full of holes to a smaller one that is easier to control, mend and redeploy.

What Does Casting Too Wide of a Marketing Net Look Like?

One of the clearest and most direct ways companies can determine if their marketing spend is in or out of line with best practices is through comparisons with best-in-class organizations. The percentage of the overall budget tied to marketing can change greatly between certain industries, and even different elements of the retail world, but a review of the strategies used by leaders within your specific vertical can provide a strong, easily understood starting point.

It’s also important to remember that marketing theories and processes regularly evolve and change, especially with so many digital methods for analysis, outreach and engagement now available. Some small, tech-savvy firms and niche e-commerce players outperform leaders in their industries in terms of return on investment and market penetration, despite smaller budgets. That happens thanks to an increased focus on using the most effective information, systems and avenues to connect with high-value customers.

A too-large marketing net can also stem from a failure to use market segmentation. A lack of specialization and personalization, especially for campaigns that target large groups of people or geographic regions, simply isn’t acceptable when so many potential competitors regularly use and benefit from such tactics.

A data-driven approach to marketing is now possible for businesses large and small, but many companies avoid it due to perceived cost issues and only focus on that element when designing marketing efforts. A method that only concentrates on budgets is ultimately short sighted. It keeps costs lower in the short term; however, it also excludes the very positive developments that come with increased understanding of your true customer base and marketing to them effectively across many channels, which provides a particularly valuable revenue stream for a variety of organizations.

Enterprises must understand the value data provides and invest in market segmentation efforts that help define potential audiences and allow businesses to market to them effectively.

Casting a Smaller Net for Stronger Results

Personalization and developing a deep, regularly updated understanding of potential customers through effective use of data are critical for modern marketing success. Businesses need to have a strong grasp of what appeals to large swaths of their customer base, with similar information about smaller groups and individuals. This data-driven approach ultimately provides the information companies need to stage effective multichannel marketing campaigns that make the most of each platform used for outreach and target specific consumer sentiments and desires.

Perhaps most importantly, this strategy allows businesses to target especially high-value customers repeatedly in ways that remain relevant to the shopper through personalized appeals and outreach. Making sure marketing messaging stays relevant over time, especially in the context of engaging frequent shoppers and top spenders, is just as important as engaging new customers. Using data as the crux of such efforts gives companies valuable, accurate analysis that can inform marketing campaigns and make messaging delivered many years into a customer-business relationship just as relevant as the first piece of outreach.

Shifting to smaller, more targeted efforts isn’t only about attention and communication on the individual level, either. Organizations with a brick-and-mortar retail footprint can go beyond marketing efforts that target their entire customer base to focus more keenly on smaller areas, including the catchments around individual stores. One discount general merchandise retailer shifted from a national advertising strategy to one tailored to each store’s individual customer base. By identifying relevant demographics and the impact they have on purchasing at each location, the retailer can now focus more on areas with a higher proportion of potential frequent shoppers to derive the most value from their marketing spend.

Of course, simply having relevant and accurate data on hand isn’t enough to craft a smaller net that is more effective at targeting specific customers. Businesses also need efficient processes for analysis and developing relevant understanding of those results across an organization. Companies need to have the tools and knowledge on hand to readily leverage data to define, segment and evaluate customers. They must also have the capacity to make these findings quickly and accurately. The rapidly changing nature of consumer preferences in the modern economy means too long of a delay when gathering and analyzing can cause significant problems in terms of relevance and the overall value of related marketing spend.

Ultimately, creating and defining your smaller, more effective net stems from a desire to maximize ROI and get the most value possible from every marketing dollar. That means an emphasis on ensuring campaigns reach their intended targets, drive sales and have clear messages and missions. By avoiding waste and minimizing redundancy in a marketing budget through the context provided by a data-driven approach, alongside the many advantages provided by the results of successful, personalized and targeted campaigns across many channels, business quickly realize benefits from this more streamlined strategy.

Starting to Make More Effective Marketing Changes

One of the greatest challenges of making a major business process change, such as a data-based strategy for marketing, is generating awareness and acceptance. Luckily, many companies already recognize the need to better understand customers in more granular detail, quantify their value and more effectively and appropriately target them. Companies that don’t change their approach will only continue to fall behind both industry leaders and smaller players that recognize the value of data in a marketing context and use it to effectively reach out to their customers.

The Cost of Marketing to the Wrong Consumer, and How to Get It Right

We all know that Internet marketing is easy and cheap. But regardless, marketing to the wrong retail customer can come at a high price. Here are some suggestions for how to keep your marketing judicious and well-targeted, so you’re reaching the right audience.

Internet marketing is easy and cheap. That’s all the more reason to use it judiciously, because the cost of marketing to the wrong retail customer can cost big. Here are some suggestions to make sure you’re targeting the right audiences.

Effectively used, marketing has the power to connect the right consumers with brands and turn them into loyal, repeat customers. But what happens when it’s not, and what’s the cost incurred? Bigger than you think — bad campaigns are deadly on a number of fronts. It’s not just lost sales. They result in lost loyalty and a confused target market. They can quickly alienate some of a retailer’s most valuable potential and current customers. That leads to further difficulty attracting and maintaining relationships with the very people who could have been your best customers, brand ambassadors or social media amplifiers.

Because it’s easier to reach out in today’s digital environment, retailers can more easily connect with their client base now than ever before, for better or worse. Just because they can, doesn’t mean they should. It’s very easy to try a new type of campaign or use digital tools like social media, but it’s just as simple for poor planning and execution to lead to a negative result.

With the rise of digital marketplaces and the vast increase in shopper options, the way shoppers buy products has drastically changed. This means that retailers must regularly adjust, refine and improve their approaches to marketing. It’s critical to understand that just using the internet as a marketing tool isn’t enough —it’s easy to market in a tone-deaf manner. As with any other campaign, success depends on careful planning during every stage of development and the judicious use of accurate, current data and relevant analytics tools. When marketers don’t do this, they risk the consequences of directing their marketing initiatives at the wrong consumer. And there are far too many marketing strategies that don’t lead to the generation of value or a customer transaction.

What Sets Great Modern Marketing Campaigns Apart?

It starts with careful and thoughtful direction of resources involves gathering data, collecting and securely storing it, and effectively using analytics tools to derive useful, actionable insights that form and bolster relationships. Drilling down, certain qualities of effective marketing campaigns set them apart from other, less-successful efforts. Here are a few of the most important concepts for reliable, powerful and positive results:

  • Focus on a well-defined customer type: Great campaigns don’t cast too wide a net. Instead, they have a clear idea of whom they’re targeting.
  • Don’t worry about long-tail keywords: Unless your company can compete with the giants of your market segment — and giants of every segment, like Amazon — it’s best not to put too much stock in these keywords.
  • Emphasize qualified leads: A qualified, well-understood customer persona is much more than an email address. With a thoroughly developed customer profile, including data about budgeting and identity, companies have better results. This is one of many areas where powerful, effective analytics comes into play.
  • Align large and small details to the defined personas: A strong campaign should feel relevant, attractive, focused and engaging to its recipients.
  • Segment your database, continually: Building the difference between prospective and existing customers into targeted variations of the same campaign, for example, helps retailers realize the best results. Continually segmenting databases through the use of effective big data and analytics tools is one difference that sets retail leaders apart from the rest of the pack.
  • Properly value existing customers: You already have a stronger relationship with existing and past customers than with potential ones. An incentive like a coupon or discount — with the exact terms defined in part through analytics and big data — is often enough to secure a new purchase.
  • Gather feedback: Valuable intelligence about your products, customer service and brand experience comes from social media and many other online communities. Retailers need to be where their customers congregate online, then gather feedback for review by staff and use in automated analysis.
  • Build emotional connections: Lasting, meaningful connections with core customers are more important than customer service in many instances. Building these relationships means encouraging purchasing over the long term. Consider these examples:
    • Target determined it was too narrowly labeling bedding and toys for children based on gender. Taking changing attitudes about gender fluidity into account, the retailer stopped marketing based on gender. It now markets bedding and toys with a more inclusive strategy.
    • Dick’s Sporting Goods announced it would stop selling assault rifles and raise its minimum age for purchasing firearms to 21. CEO Edward Stack decided this would provide an overall benefit and strengthen bonds with customers throughout all of its product lines.

A large part of the fine-tuning involves drawing on the power of data and analytics to ensure they can move at the speed of the modern consumer and connect to them effectively. Many aggressive, short-term campaigns use crowdsourcing, social media and apps to build strong, short-term connections. Carried out properly, these efforts increase positive sentiment among the customers you know are interested in shopping with your company. This turns the digital world into an invaluable public space in which businesses can interact with customers, using existing and custom-built tools to quickly and efficiently reach them. The costs of marketing to the wrong consumer are both clear and substantial. So focus on your current and prospective customers and leverage big data and analytics tools to market to the right ones.