It's no secret that marketers need to make the most of their customer data, especially considering the importance of creating relevant, enjoyable omnichannel experiences for today's consumers. Location is a pivotal component of an effective omnichannel strategy — the challenge is identifying where your investments will yield the greatest returns for your business.

Location intelligence refers to a suite of geospatial analysis techniques enhanced with predictive customer insights. Whether you're considering increasing investment in your existing markets, expanding to new ones, or looking to drive foot traffic to specific retail or branch locations, incorporating propensity scores into the following geospatial analyses enables your business to identify which areas have the best opportunity to maximize ROI on geographical expansion, geo-targeted marketing campaigns, and much more.

Three predictive geospatial analysis techniques for your location-based marketing strategy

Every geography is different when it comes to the types of customers who live there and how they choose to engage with your brand, products or services, and marketing initiatives. Sometimes these geographic differences are negligible, but often there are important distinctions that can help you improve out-of-home campaigns or store placement. Here are three predictive geospatial analysis techniques that can help maximize your returns.

Trade zone analysis

"Who lives near my business's location, and how far are they willing to travel to get there?"

Predictive trade zone analysis considers the location of an existing or proposed business site. The analysis uses customer and geographic data to establish which customers and prospects live within a certain radius of the location and how far they're willing to travel to get there.

Leveraging the results of a trade zone analysis, you can better identify which customers within a given area you should target, whether that's with a direct mailer, a phone call, or even an email drip.

Market and hotspot analyses

"How can I efficiently expand into new markets?"

Predictive market analysis, also known as market sizing, is used when you want to understand the level of opportunity and/or site-suitability in a particular city or neighborhood. Though not limited to offline initiatives — you can also identify high-value areas of online shoppers to optimize your keyword bidding strategy for SEM campaigns — this analysis is often useful when establishing an offline experience (e.g. new storefront, pop-up shop, or billboard).

A hotspot analysis — a component of a predictive market analysis — identifies high-density areas of your target customers and ranks the geographies where your business would perform best. Depending on your growth goals, the scale of the prospective area could be as large as the entire nation or as small as a specific neighborhood.

Penetration analysis

“What is the realistic opportunity for growth in a given geography?”

Unlike the two previous geospatial analyses, a penetration analysis does not predict market performance so much as it reports on existing performance to give you insight into how you should prioritize your resources. The most valuable insight derived from a penetration analysis is the opportunity index, which suggests the percentage of likely-to-act consumers in a given market. It essentially identifies markets where your business has the greatest potential to maximize returns on future investments.

A penetration analysis can be supplemented with a subsequent market analysis to educate you further about customers and new opportunities in smaller areas of interest, like specific zip codes or neighborhoods.

Importance of physical retail in effective omnichannel commerce

Establishing a brick-and-mortar presence can be expensive, and it's often difficult to execute efficiently. In the past, retail siting and market sizing efforts have been outsourced to slow-moving consultants or have been founded on educated guesses, rather than data-driven predictions.

Today we're hearing a lot about the “retail apocalypse,” where longstanding big-box stores are shuttering thousands of stores as consumers increasingly shop online and engage with direct-to-consumer brands over traditional retail. But as larger stores close locations, digitally native brands are turning towards brick-and-mortar retail as an additional revenue source — and they're finding a high rate of success. According to Forbes, these brands have plans to open more than 850 new locations in the next few years.

The same report notes that “when a retailer opens a new store, on average, that brand's website traffic increases by 37%, relative share of web traffic goes up by 27% and the retailer's overall brand image is enhanced.” This so-called “halo effect” is often improved with location intelligence.

If you're looking to expand your own business into new areas, temporary retail initiatives — pop-up shops, showrooms, and multi-brand partnerships — provide you with the opportunity to explore different markets at a lower risk. But because there's not always the same draw as a full-fledged brick-and-mortar presence, you still need to be intentional about where you place these temporary offline experiences and how you curate them for each geography. What may be successful with consumers in one city may not engage a different city's consumers as effectively. Employing predictive geospatial analysis techniques can help you understand the nuances in your customers' offline shopping behaviors.

Impact of location intelligence on marketing and advertising

Location intelligence isn't limited to physical retail initiatives. The insights generated from these predictive geospatial analyses are often applicable to both traditional and digital marketing initiatives.

If your business operates in a variety of locations, it's often safe to assume the kinds of customers you serve will vary from place to place.

For example, if you renovate homes and build audiences for marketing campaigns targeted around what kinds of houses your potential customers live in in Texas, that same audience criteria probably won't work in New Jersey. Why? Well, most houses in Texas don't have basements. And if you're using that criteria to target homeowners in New Jersey, where many houses do include this feature, you could miss out on a number of potential customers.

With direct mail campaigns, canvassing, or even phone banking, leveraging household details — from family size to type of home to a roof's solar suitability — on your leads can reduce your customer acquisition costs. Instead of targeting neighborhoods or cities en masse, you can identify specific homes that meet the criteria of your target audience.

A similar effect happens when you leverage geospatial insights on your customer base if you're thinking about implementing subway, bus ads, billboards, or any other out-of-home experience into your marketing strategy. Identifying high-opportunity areas and understanding how consumers in those areas compare to their neighbors or the greater population enables you to tailor your ads to those who are likely to engage with them.

Online initatives like search engine marketing (SEM) can also be bettered with the use of geographic and customer data. For example, putting bid modifiers on high and low-opportunity zip codes helps optimize your overall spend.

Grow intelligently

As consumers continue to engage and shop across a variety of channels, failing to fully explore both online and offline opportunities can be the Achilles' heel of companies with even the highest potential for growth.

Location intelligence is just one piece of an optimized omnichannel strategy, but a vital one. Right now there is more access than ever to consumer and geographic data, and the businesses that are scaling efficiently in today's competitive markets are capitalizing on the insights that data provides. How is your business growing intelligently?

You've probably noticed the growing hype around artificial intelligence (AI) in marketing. From chatbots to content creation to programmatic advertising — it seems like every other MarTech or AdTech platform is baking in some sort of AI capability.

With so many applications, it's easy to lose sight of what's most important in implementing an effective, optimized marketing strategy: deeply understanding your customers.

According to Forbes Insights, only 13% of businesses express a high degree of confidence that they are making the most of their customer data.

Stepping back, what does AI even mean for marketing?

At a high level, AI refers to a computer's replication of some aspect of human intelligence — pretty ambiguous, right? AI, as it exists today, is an umbrella term for a range of computer-enabled data analysis techniques — the most relevant and widely-practiced in marketing being machine learning.

Machine learning (ML) is the process of training computers to “learn” to recognize important patterns and trends in large datasets, with the goal of developing data models that can quickly categorize new data inputs and predict likely outcomes.

So, what does that mean for you, the customer-centric marketer? When using your customer data, or training data, as the basis of machine learning models, you can start to generate deeper customer insights and make better behavioral predictions. These can be around your prospects' and customers' likelihood to convert on certain campaigns, increase their purchase frequency, churn or lapse, or something much more specific.

Leveraging machine learning in your marketing strategy is no longer a luxury — it's a necessity. As competition increases and ad space gets more crowded, consumers have more choices of businesses to engage with, making machine learning critical to efficiently reaching the right people and keeping your customers engaged.

ML-driven insights marketers can't ignore

It should come as no surprise that the world's top brands are efficiently scaling growth by leveraging machine learning to prioritize their resources and personalize experiences across their customer lifecycles.

Here are some of the most important ML-driven insights marketers are using to craft better customer experiences and optimize their performance.

Behavioral insights and predictions

A vital piece of giving your prospects and customers a memorable experience with your brand is knowing who to engage with and when.

Have you ever been in the position as a consumer where you're targeted with ads that don't align with who you are or where you are in your customer journey? Perhaps you see an ad trying to get you to buy an item you've already bought, or offering you a first-purchase discount code when you're already a customer.

As a consumer, these experiences can be annoying and frustrating. And as a marketer, your message can end up diluted instead of impactful.

Behavioral predictions are crucial to proactively engaging your customers. Rather than reacting to your customers' behaviors, you're able to anticipate them and market to your most valuable customers at the right time, with content that corresponds to where they are in their journeys. This helps you prioritize your resources, optimize your marketing spend, and cut through the noise to better reach your target audience.

How can machine learning help predict customer behavior?

Relatively straightforward machine learning algorithms can uncover predictive patterns hidden deep in your customer data. The random forest, or random decision forest model, analyzes your historical customer data, building a series of decisions trees to predict the likelihood that future inputs (e.g. new leads) will result in a target outcome (e.g. make a purchase).

If you advertise on Facebook, you've likely come across lookalike audiences, which are generated in a similar way. Using lead conversion as the example outcome, these models essentially predict the degree to which a new lead best “looks like” leads who've successfully converted to customers in the past.

Naturally, the data used to train or build your behavioral models will influence how they make predictions. Done properly, these predictive insights can have a tremendous impact on your return on ad spend, overall customer acquisition costs, and customer satisfaction.

Persona-based insights and predictions

While predicting your customer's next move is immensely helpful in reaching the right people at the right time, it's not where the road to truly optimized marketing ends. Effective and thoughtful engagement requires an understanding of who your customers are as real people, so you can create hyper-personalized experiences that evoke emotional responses.

Salesforce Research revealed that 84% of customers say that being treated like a person is very important to winning their business. If you show your audience that you understand what motivates them to interact with you, whether they're an early prospect or a loyal customer, the relationship and trust between you and your customers grow stronger.

So how can machine learning help you personalize experiences at scale?

Customer clustering

Buyer personas, semi-fictional representations of your target customers, are instrumental in creating personalized content and creative that truly resonates with them. Traditionally, personas were created using basic demographic data, some psychographic data from surveys or focus groups, and a good amount of human intuition. While this approach has worked for years, it leaves too much room for human bias.

Customer clustering leverages a different machine learning technique than behavioral modeling: unsupervised machine learning. Rather than uncovering patterns that are predictive of a known outcome (e.g. likely to convert), unsupervised algorithms, like K-means, sort data into distinct groups based on shared attributes. The resulting groups, or clusters, form the foundation of unbiased, truly data-driven personas.

As time goes on and new data is collected, running the clustering algorithm again may reveal new emergent personas, enabling you to refresh your messaging, creative, and other personalization efforts to stay relevant as your customer base evolves.

Burrow, a disruptive direct-to-consumer furniture brand, uses ML-driven personas to identify what color couches their audience segments see in targeted ads. They found that customers who are older, live in single-family homes, and have kids are more likely to buy couches in darker colors; customers who are younger, live in apartments, and have few or no kids are more likely to buy couches in lighter colors. With these insights, Burrow was able to push creative that reflected these attributes to the audiences that possessed them.

Location-based insights and predictions

Virtually all consumer-facing companies need to consider location in their marketing strategies, whether it's out-of-home advertising, brick-and-mortar marketing, or geotargeted search campaigns. However, these tactics are often expensive and difficult to do well.

Geospatial intelligence refers to a suite of geospatial analysis techniques enhanced with a combination of behavioral and persona-based insights. Whether you're considering increasing investment in your existing markets, expanding to new ones, or looking to drive foot traffic to specific retail or branch locations, incorporating predictive insights into your geospatial analyses can drastically improve ROI.

• Predictive penetration analysis aims to identify the maximum return you can expect from further investments in your existing markets. The resulting insights can help you understand the performance of your existing sites and determine whether you should increase investments or cut back entirely.
• Predictive market analysis identifies specific hotspots of existing customers and customer lookalikes to guide where you should put new sites and optimize acquisition-focused out-of-home and geo-targeted SEM campaigns.
• Predictive trade area analysis identifies the maximum distance customers are likely to travel to your existing retail sites. The resulting insights can help you optimize individually-targeted and geo-targeted campaigns focused on driving foot traffic to those sites.

Implementing machine learning

While several complex applications of AI are still years — or even decades — away from being fully developed, the democratization of machine learning is enabling nimble marketing teams to generate these predictive customer insights without having to spend millions on expensive consultants or hire large data science teams.

If you're considering adopting machine learning or looking for ways to expand your analytics team's bandwidth, our article, Is your marketing team AI-ready?, discusses important considerations to ensure successful implementation.

Local Artificial Intelligence Company To Work With State Of Vermont To Help Grow Workforce

BURLINGTON, Vt., Feb. 13, 2019 /PRNewswire/ -- Faraday is pleased to announce a strategic contract with the State of Vermont to use their platform to drive more interest and engagement in people looking to relocate to Vermont. This technology will be used in conjunction with the Vermont Department of Economic Development's ThinkVermont initiative and website, which engage a wide-ranging audience around opportunities to live and work in Vermont.

Read more here

Over the last 45 years, Peacock Alley has established itself as a leader in high-end, luxury linens and bedding by placing their valued customers first, from product design to enjoyable, evolving shopping experiences.

Peacock Alley recently signed on with Faraday to leverage artificial intelligence and nationwide consumer data to continue to deepen their understanding of their current customers and intelligently target and acquire new customers.

Ryne Higgins, Head of eCommerce and Digital Marketing, spearheaded the efforts to incorporate AI into Peacock Alley's marketing strategy. We had a chance to speak with Ryne about Peacock Alley, their decision to implement an AI solution, and what their onboarding experience has been like so far.

We'd like to thank Ryne for sharing his thoughts and experience, and hope you enjoy the interview!

Can you provide a brief history on Peacock Alley?

Ryne:

"Our founder, Mary Ella Gabler founded Peacock Alley in 1973 (yes, we are 45 years old and working with an AI / machine learning software!) on the "Little Black Dress" theory to bedding and bath linens; own the best basics and then thoughtfully work everything else in.

About 9 years ago, she handed the reins over to her sons – Jason and Josh - and they are continuing to run the business today with her watchful eye still on product development and brand.

The company has been through a lot of iterations throughout the years but our core business channels are perhaps not that much different than you'd expect – we have a growing wholesale, retail, and eCommerce presence still today."

Who is a typical Peacock Alley customer? Is your customer base changing?

Ryne:

"The typical Peacock Alley customer is a difficult question and one of the reasons why we have paired with Faraday.

Across our channels, you may get a lot of answers. For example; our wholesale channel sells to a number of specialty stores, online dropship channels, and direct-to-designer. Our retail stores, while open to the public, mostly focus on the professional, accredited designer who is looking for the touch and feel experience in Dallas, Austin, Atlanta, or Nashville.

For my purview of the business (eCommerce), we are mostly focused on striking the cord with the do-it-yourself designers (think of what you see on Pinterest and beyond), whether they need coverlets and bedspreads, bath towels, or luxury fitted sheets.

I wouldn't say that our customer base is necessarily changing – we're still committed to the people that have made us successful over the past 45 years – however, as a brand, we are always interested in understanding what makes our customers tick and finding ways to attract new customers."

What does a typical Peacock Alley customer lifecycle look like? For example, are a lot of your customers repeat buyers? How do you communicate with past customers?

Ryne:

"Like a lot of 45 year old brands, our changes in the online space have been more revolution vs. evolution. In the past year; we've redesigned / replatformed our website, paired with companies like Faraday, and have really been trying to reshape our marketing mix between traditional and digital channels. For digital channels, we are seeing a high rate of new customers as we've built an experience that they are interested in interacting with.

We see very strong customer loyalty and to be frank with you, our product and brand has really stood on its own in bringing customers back historically. Our messaging and retention strategies have really been based more on the personal relationships we've built with our customers over the years.

As a brand with high-touch customer service and interaction, we are always looking for ways to supplement the old-fashioned "how are you doing" with digital communication that adds value to our customer's experience with the brand."

How has your marketing strategy evolved over the last 5 years? Where have you found success? Where have you struggled?

Ryne:

"Without getting into too many details, I will say that it is definitely in a place of evolution. Our business was built on the wholesale channel and sales tools, swatchfolios, etc have always been a staple of our marketing efforts.

That is still true today but we are also trying to find ways to improve those assets while also mixing in digital marketing (be it SEO, paid search, paid social, etc). As with most traditional media efforts, tracking can be difficult compared to the relative ease of digital. Faraday's toolset will allow us to continue to close that gap."

To what extent has data guided your marketing strategy throughout that period?

Ryne:

"Data and the ever-evolving digital landscape has been paramount in our evolution of our marketing strategy. When Mary Ella started the business, you could have never imagined directly tying a dollar spent to a dollar earned in marketing.

As these tools and technologies continue to evolve, for us it is about finding ways to marry the digital tools out there today with the traditional efforts that have really built and grown this business over the years."

Can you describe your current tech stack from your e-commerce system to customer data management? Has this changed significantly over the last few years?

Ryne:

"Sure. The fun thing about digital is it is pretty easy to find out what tools and technology people are using so it's pretty much public information.

Last year, we made a transition from Magento to Shopify, we signed on with a tool that allows us to visualize the true color of a product without having to shoot our products in a dozen different colors, and have signed on data and machine learning partners like Faraday.

We believe that to scale the marketing efforts of a luxury business through digital – we had to have a partner that could help us solve for the black box of channels like Facebook and Instagram.

It is pretty unbelievable that, with as great of a platform some of these channels are, they can only build look-a-like type prospecting audiences with 2.1M+ people in them, making it very difficult to understand why something is performing or not.

By leveraging a partner like Faraday, we have the ability to pull back the curtain and communicate with members of their account management, leadership, and theoretically their engineering team if ever needed.

For us to make significant adjustments in our marketing budgets, we need the ability to find the right people and send the right message at the right time. It is a fairly cliché statement but partners like Faraday should help us close that gap."

Prior to learning about Faraday, had you considered leveraging AI in any aspect of your business? If so, how? If not, why?

Ryne:

"I mean, sort of? I come from a background that is extremely digital in nature and I'm constantly trying to follow the trends that will help us build our brand. I will say – before talking to Faraday – I didn't think that AI was accessible to brands like ours quite yet (we have a small, scrappy team).

I was extremely impressed with what a relatively small team in Vermont can produce, and I liked their ability to "flex" their messaging to someone who considers themselves digital savvy but would have never considered scaling an in-house data science team in the short-term."

Why did you choose to leverage Faraday's AI solution?

Ryne:

"I've said a lot in the rest of the interview that I think answers this question but I would also say that the ability to take our use cases and personalize their product to what we needed to accomplish was key.

The Faraday team (shout out Robbie) is the type that listens, and I genuinely got the feeling that, in a world of technology partners who say a lot, they actually had the ability to walk the walk."

Can you describe your onboarding experience with Faraday?

Ryne:

"I've been through countless onboarding meetings and they are often met with a disconnect between sales and account management. It seemed like sales and account management was in sync. They had communicated our efforts, asked for refreshers as needed, and without the need for constant follow up have been meeting the quick deadlines that we needed them to.

As a business manager, I'm constantly met with "how" or "why" type questions and the team has been very responsive and thorough. It really sets the stage for our entire engagement."

We're excited to be working with you! Do you have any other comments or suggestions you'd like to add for companies looking to get started with AI?

Ryne:

"This might sound weird from a customer of the company, but don't just sign up for AI because you'd like to say you work with an AI firm. Take the time through the process and see if you can link Faraday's AI solution to your business outcomes.

Consider your ability to execute with the platform. We've been waiting for the solutions that we think Faraday can solve for and it seemed like a great fit for our business."

Thanks a bunch, Ryne!

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