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What is predictive geospatial analysis, and how does it impact location-based marketing initiatives?

geospatial insights and intelligence
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
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 hotspot analysis
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?”
market penetration analysis
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.

geospatial insights for retail strategy

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.

geospatial insights for marketing strategy

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?

burrow omnichannel case study

Is your marketing team AI-ready? 3 considerations for consumer marketers

No doubt you're hearing about the power of AI to transform marketing. But if you're like many marketers, the prospect of integrating artificial intelligence (AI) into your strategy may be daunting. It's a rapidly evolving field with seemingly endless applications, and you may have had relatively little experience with AI to date.

However, if you're committed to a data-driven marketing strategy, it's worth getting acquainted with AI and machine learning (ML); doing so will allow you to further monetize your existing data, and put the immense power of big data to work for your business.

According to Capgemini Consulting, 3 out of 4 organizations using AI and ML increase their sales and enhance customer satisfaction by more than 10%. Marketers already use AI to optimize various initiatives and automate repetitive, time-consuming tasks. Some applications include:

  • Audience expansion (AKA lookalike targeting)
  • Personalization at scale
  • Programmatic advertising
  • Automated content creation

If you're curious about what AI can do for your marketing team, but still trying to decide whether you're ready to implement the technology, here are three crucial points to consider:


1) Goals and strategy: Which outcomes do you want to optimize?

Establishing clear goals will help you make decisive choices about which applications of AI are best for your team, which data sources to tap into, and how to implement the technology with the resources at your disposal.

Are you more focused on optimizing customer acquisition, turning one-time buyers into repeat customers, or perhaps something much more specific?

If your goal is to optimize ad spend across your digital campaigns, you might consider leveraging applications like propensity modeling and programmatic advertising. If your goal is to drive upsells or cross-sells amongst your existing customer base, propensity modeling and personalized offers will be more helpful.

It's important to clearly define your target outcomes before making decisions about how to apply AI within the context of your particular marketing strategy. Then, research and strategic planning will help you decide which solutions are worth prioritizing in pursuit of your established goals, and which will involve more trouble and cost than they're worth.

2) Data: Is your data AI-ready?

Data is the fuel that powers AI. Quantity and quality will ultimately determine the effectiveness of your AI applications. This means it's critically important to ask whether you have enough historical data to train your AI — typically, at least 1,000 "past examples" — and whether your data is clean and rich.

Does your data have sufficient breadth and depth? More specifically, how many attributes and events are being considered per record, and are those additional data points predictive of the outcomes you want to optimize?

Thanks to big data vendors, consumer marketers don't need to rely purely on first-party data to train their AI models. Enriching existing lead and customer data with third-party consumer data can improve the predictive accuracy of your AI applications by an order of magnitude.

Here are a few ways to ensure your data is AI-ready:

  • Identify predictive data sources: If the goal is to increase lead conversion rates, your ESP and e-commerce platform will likely contain the most predictive data. Lifecycle mapping is a useful exercise to help you discover which data sources are most predictive of a target outcome.

  • Do a data audit: Clean your existing data by eliminating inconsistent, incomplete, or duplicate records. You can do this in-house or hire a consultant. Alternatively, certain AI platforms and third-party services can help automate the process.

  • Add depth and breadth: Enrich your first-party lead and customer records with third-party demographic, behavioral, and property-based data. While licensing third-party data can be costly and time-consuming, certain AI platforms and third-party services include third-party data and automate the data enrichment process. The Faraday Identity Graph is a prime example.


3) Implementation: Which approach makes the most sense for your team?

The resources needed to operationalize AI depend on how you choose to implement your solution. Business Insider's AI in Marketing report lists three common approaches: building in-house capabilities, using a third-party service, or using a platform. Each approach has its pros and cons, so it's important to carefully consider whether a solution's benefits outweigh implementation time and costs.

In-house capabilities

This is the most customizable, yet most resource-intensive approach to operationalizing AI, and should only be considered if your entire company is committed to an AI-first mindset. At minimum, building in-house capabilities from scratch requires:

  • Rich training data
  • Data storage capabilities
  • ML algorithms
  • Data science automation systems
  • Deployment features and integrations
  • Experienced talent (data scientists, ML experts, and engineers)

Third-party services

For companies that require numerous AI applications across different business functions, but don't necessarily need to feed insights and predictions between applications, third-party services like IBM's Watson can be a good alternative.

These services help facilitate AI implementation by including data science automation systems and built-in ML algorithms that in-house data science teams can use to build AI applications for marketing, accounting, SCM, or any other function worthy of optimization and automation. At minimum implementing AI with a third-party service requires:

  • Rich training data
  • Deployment features and integrations
  • Experienced talent (data scientists, ML experts, and engineers)

AI platforms

Platforms are designed to streamline AI implementation for relatively specific sets of use cases. ML capabilities and features are built in to platforms and supported by data science teams, enabling marketers to easily interact with, and act on AI-powered insights and predictions through the platform's user interface.

While specific requirements will vary depending on the platform, at minimum, operationalizing AI with a platform requires:

  • Basic training data

So, is your marketing team ready to implement AI?

If you have enough customer data, there's probably a solution that will fit your needs, but choosing the right implementation approach for your specific objectives can be challenging. If you're having trouble, consider the following questions:

  • Is your company committed to an AI-first strategy? If so, where do your marketing objectives fit into the strategy?
  • Does your company already have an in-house data science team? If so, do they have the bandwidth to focus on your marketing objectives? If not, do you have the resources to recruit and hire the right talent?
  • How important is customization? Will your AI-powered marketing insights and predictions help optimize AI efforts in another business function?
  • What's your timeline? In-house capabilities generally take at least 18 months to build from scratch, third-party services can take between 12-18 months depending on customization requirements, and platforms will take 6-12 weeks depending on predictive modeling requirements.

How to build your own consumer marketing AI stack

If customization is a priority and you have the resources to build in-house capabilities or use a third-party service, we've created a guide to building your own consumer marketing AI stack.

Alternatively, if it seems like your team should be using a platform, the guide will give you a good understanding of the data, systems, and features included in Faraday's AI platform.


AI for a better B2C acquisition strategy [3 advantageous applications]

AI for B2C customer acquisition

More customers, more problems? That doesn't sound right

Traditionally, customer acquisition strategies were guided by industry best practices and experiential knowledge held by an organization’s executives and senior-level individuals. Sometimes these strategies were successful, sometimes they were not.

Success was primarily determined by the return on investment (ROI) for various acquisition initiatives. While ROI is still one of the best ways to gauge the effectiveness of your efforts, the “trial and error” approach can have severe impacts on revenue growth, especially for small and midsize companies.

Machine learning, a subfield of AI, enables companies to take much of the guesswork out of the customer acquisition process, and it all starts with good data.

Extracting value from your data

B2C companies have access to an abundance of consumer data — data that enable companies to draw deeper insights about what their best customers look like, which leads are worth targeting, which types of promotions are likely to keep them engaged throughout the buying process, and just about any other outcome companies want to optimize.

As companies consider more data, they require more efficient means of analyzing it. That’s where AI becomes invaluable, as it allows companies to draw deep, data-driven insights and make accurate predictions about target outcomes. However, the key is implementation, leveraging these insights and predictions efficiently throughout the acquisition process.

3 applications of AI for B2C customer acquisition

1) Propensity modeling

The best way to optimize any customer acquisition initiative is to focus your resources on individuals with the highest propensity to convert on a target outcome. Why waste your precious marketing dollars on leads that probably won’t become customers, or worse, spend more on acquiring a new customer than they spend on your products or services?

Through various machine learning techniques, data scientists can build models capable of predicting — with a high degree of accuracy — a lead’s propensity to convert on a target outcome. These propensity models are especially helpful in optimizing lead generation and customer acquisition campaigns in two ways:

  • Predictive targeting: Propensity models “score” individuals on the likelihood that they will convert on an outcome. Outcomes can be as broad as “make a purchase” or as specific as “bought product X and product Y, but not product Z.” Propensity scores become actionable when they’re fed back into a CRM, ESP, e-commerce platform, or any other marketing system. As you might imagine, targeting likely-to-convert audiences helps drive conversion rates and ultimately improve ROI on acquisition initiatives.

  • Personalization: You achieve the greatest performance improvements when targeting propensity-based audiences with highly-personalized campaigns. Identifying the most influential variables in the modeling process helps you draw qualitative insights, which you can leverage to personalize and optimize qualitative aspects of your acquisition strategy: product photography, ad creative, web copy, etc.

The role of AI in personalization whitepaper

2) Real-time lead scoring

The sooner you identify an individual’s propensity to convert, the better you can allocate your resources. Real-time scoring integrations feed new lead data into propensity models, where they’re scored and returned to various marketing systems. Real-time lead scoring can help you optimize your customer acquisition strategy in three ways:

  • Immediately determine whether the lead is worth further marketing or sales engagement. If not, save your resources for high-scoring leads.

  • Immediately enroll high-scoring leads into the right nurture campaigns. If a likely-to-purchase lead was captured on a particular product page, you might want to enroll them into a promotional campaign for that product.

  • Immediately inform your sales team when high-scoring leads enter the funnel. This is especially useful when using multiple propensity models for multiple outcomes. If a new lead has a higher propensity to purchase product X than product Y, your sales team should know as soon as possible.

3) Location intelligence

Location intelligence situates business data in a geographic context. Identifying "hotspots" of high-scoring individuals can significantly reduce customer acquisition costs in two ways:

  • Retail site and showroom expansion: Physical, or "brick-and-mortar," locations carry tremendous costs. It's no secret that location is paramount to a site's success. With scored geographies and careful analysis, you can identify the best (and worst) locations for new sites.

  • Service area geotargeting: Whether the goal is to drive foot traffic to physical sites or to maximize ROI on direct mail or canvassing campaigns, targeting spatially-clustered areas of high-scoring individuals will increase conversion rates and lower acquisition costs.

Where to start

Regardless of which application of AI is most relevant to your business, start by acquiring rich, high-quality data, and lots of it. Good training data fuels the models that give you the insights and predictions needed to optimize your acquisition strategy.

When you have the right data, and enough of it, you’re ready to train your AI. You’ll need experienced data scientists and a solid machine learning engine to build models that actually work, but once they’re built and predicting accurately, you’re ready to start optimizing.

For a deeper dive into data discovery and machine learning, download our Customer lifecycle optimization whitepaper. It lays out the four-step process that hundreds of B2C companies use to operationalize revenue-building AI, from customer acquisition to retention.

Customer lifecycle optimization whitepaper