Enhancing Customer Insights with Public Location Data

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The pervasive adoption of mobile devices has driven an explosion of contextual user information, including geolocation data, which has become a valuable resource for marketers. However, a lack of technical skill sets among marketers has made it difficult for them to use this data (when they have access to it) effectively. Plus, changing regulations mean it’s more important than ever for marketers to understand what data they have access to and how to properly leverage it.

Currently, most brands, agencies, website publishers, and other marketing entities use location data to engage in a variety of marketing applications, such as proximity marketing, among shoppers with the brand’s app. Many retailers and proximity marketers have deployed beacons inside stores that have resulted in up to a 15% lift in retail foot traffic and a 73% increase in the likelihood of purchase among shoppers. Beacons are battery-powered wireless sensors installed in retail stores or event venues that detect nearby consumers who have opted in to alerts through Bluetooth or other technologies and that relay information to consumers’ mobile devices. For example, a store like Macy’s can build its presence on a beacon platform that can be downloaded by shoppers as a mobile app. After that, each time shoppers with the app enter a beacon-enabled store, they can receive promotional messages or deals on their device about products in the aisles they are browsing.

Brands also use geo-fencing, or creating a zone around a business for advertisement targeting, in different locations for targeted promotional offers on mobile devices (via any digital platform the firm as access to, such as social media, email, or text). For example, Whole Foods developed geo-fences around its stores, as well as its competitors’ stores, to target relevant audiences and achieved a post-click conversion rate that was three times higher than the national average.

Some brands are using location data for improved attribution analysis to assess marketing effectiveness. This entails identifying whether exposure to a certain promotion, ad, or specific touchpoint (such as a sales encounter) for a demographic can generate future sales. For example, Placed is a firm that provides in-store attribution analysis representing consumer visits to physical store locations. It measures both promotional tactics and audience characteristics of targeted audiences who have opted in. It uses customer location data to ascertain which promotions work, and for whom.

It’s important to understand that this type of increased monitoring warrants a corresponding increased attention to privacy needs. Once a customer chooses to participate in a social media sharing system, attention has to be dedicated to securing data storage and providing the user access to information that has been collected by brands and processed on their behalf. A brand like Starbucks can monitor posts from its stores nationwide to deploy resources directed at customers who have voiced relevant needs while still inside the business premises. For example, customers irritated with long wait times can be delivered special deals to keep them from switching to other stores. But before companies engage in any kind of location data analysis, they need to have a privacy policy and be internally clear about what data they are using and why. New GDPR guidelines in Europe will grant individuals the right to access, restrict, correct, or transfer data that companies have gathered about them and to know how their personal data is being used.

With the right guidelines in place, there’s a much greater potential for geolocation data that remains untapped. We propose combining geolocation data with social media data to create what we call vigilant marketing intelligence (VMI), a conceptual framework based on our prior academic research and observations. VMI can help firms to better use location-based social media posts for enhanced data-driven marketing.

What Is Vigilant Marketing Intelligence?

Broadly, the rising gap between new customer acquisition costs and retention costs for existing customers necessitates continuous vigilance of consumers’ purchase journeys and their satisfaction from the same. In some specialized industries, such as pharmaceuticals, monitoring consumer behavior can be a legally mandated part of post-purchase experiences, with the ultimate goal of vigilance for brand and consumer safety (such as tracking adverse drug reactions). While brands do attempt to forecast customer-related outcomes based on social media posts, the availability of location-based social media data further enhances the predictive power of future unfavorable outcomes, including customer dissatisfaction, brand switching, and churn. When such vigilant intelligence is operationalized, it can help improve customer relationships, retain customers, and expand customer lifetime value.

VMI creates a framework that integrates incident reporting data from social media posts with geolocation data of the report —that is, the physical location that the post is emanating from. For example, this happens when a consumer checks in with an app, such as Foursquare, at a location, such as a store or a restaurant, and then also tweets about what is happening in terms of an experience, incident, or service encounter. While the term “incident reporting” is frequently used in media and journalism, for marketers, a close parallel is customers’ interactions with brands, which can indicate important incidents or events, also referred to as touchpoints, micromoments, or “moments of truth.”

Many companies already monitor social media networks for posts from customers. However, adding location data for monitoring consumer behavior makes the firm’s responses more actionable in the short run and adds value in the long run. For example, tracking activity on a platform like Foursquare not only can inform a brand when customers visit specific stores and complain about wait times or products being out of stock but also presents a firm with an opportunity to respond (digitally or physically) while the customer is still inside the store. The company can then open a new counter or activate an inventory transfer between stores. Additionally, in the long run, a customer’s presence in nearby businesses or establishments can help brands cross-promote their own products and services. Knowing that a loyal customer of TGI Fridays checked in at a movie theater next door can initiate special offers to attract them to the restaurant. This can help increase short-term sales as well as build long-term brand loyalty.

Mapping adds a new layer to this type of monitoring. Several African and Asian countries have used Ushahidi’s crowd mapping technology for crisis monitoring during natural disasters, post-electoral violence, and other crises. Researchers have designed early warning systems at London’s Heathrow and Gatwick airports to estimate flight disruptions, delays, and breakdowns by harvesting complaints from location-based social media. One novel use of this location-based data is KLM Royal Dutch Airlines’ surprise campaign, where the company identified passengers who checked into its flights on Foursquare and tweeted about waiting to board. KLM conducted social media research to find out more about why the customers were waiting at the gate, whether their flights were delayed, why they were traveling, and then surprised them at their gates with personalized gifts.

Integrating such social reports with geolocation delivers two added advantages. First, the content of the communication can be interpreted within the context of physical surroundings, thus identifying if the user is sharing specifics of an ongoing service experience. For example, this data could tell you if a customer is still waiting to board a delayed flight at the airport, or if they are tweeting about a bad experience after the fact. Second, knowing the consumer’s location gives a brand an opportunity to take timely corrective actions when a customer is having a problem. For that customer still waiting at the airport, for example, the airline could reach out with text updates to keep the customer informed about updated flight departure times, continued delays, or alternative travel options.

Integrating geolocation data with social media content also helps ascertain the accuracy of shared content to validate if restaurant ratings, such as those on Yelp, are consistent with emotions embedded in tweets from restaurant locations. Significant deviations or inconsistencies at certain times or days of the week can make the ratings of the restaurant from such review platforms questionable. Location-based posts can also help monitor user satisfaction dynamically. For example, users riding in different modes of transportation — buses, trains, boats, and bicycles — can report their experiences in different cities. Information gleaned from the location-based social media posts of the travelers can then show traffic patterns, such as whether certain routes are overcrowded.

To use this location data most effectively, companies need to monitor business locations for shared social media content, identify topics of conversation and the sentiments expressed, follow time-based patterns, and either promote positive remarks from customers with the help of PR teams or have customer service teams follow up on complaints.

Challenges of Building a VMI Framework

There are three major challenges to implementing a VMI framework.

The first challenge is the precision and accuracy of available location data. While a person might be located at a specific spot with geographical coordinates, the real location is often a distance from where they are shown to be. The extent of this deviation depends on the source of the data, whether it is cell towers, Wi-Fi, Bluetooth, or GPS, as well as external factors such as urban construction density and the ways consumers update device settings. The average deviation in one study was found to be 93 feet. This deviation can make a big difference in how well marketers can execute their plans, especially in crowded cities where a small distance can change the consumer’s physical state, as well as their state of mind.

The second challenge is the voluntary nature of the shared content. It becomes necessary for service providers who are harvesting the data to ascertain its validity and define the minimum volume of feedback they consider important before triggering responses. For example, feedback from a single customer about wait times may not be sufficient to generalize the operational efficiency of the staff.

Finally, the simultaneous optimization of trust and relevance is an inherently difficult balancing act. While timely interventions or offers can make customers happy in the short run, recent awareness of Facebook’s data exposure and social media practices of data sharing with third parties such as Cambridge Analytica have led to long-term concerns about the safety of their personal data. New regulations such as GPDR in the EU aim to give consumers control over their personal data. In such an environment of heightened concern about data privacy, assurances — such as better end-user license agreement design, opt-ins, limited third-party sharing, and better deidentification processes — need to be designed to alleviate concerns about storage of data, identifiability of users, and terms of sharing with other entities. Only then can VMI succeed in fully capitalizing on consumers’ location-based social media data for better data-driven marketing and a better customer experience overall.

Mobile technology allows firms to know where the consumer is located. Integrating such location information with social media posts that the consumer shares from that location enables a better marketing intelligence system. Such a system can help firms better understand consumer journeys and also address consumer needs in the moment, provided that consumer privacy and security concerns are adequately addressed.

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46% of marketers aren’t sharing regular customer insights with sales

Almost half (46%) of marketers only share customer insights, data and feedback with sales teams at most once a month, according to a new study.

New research from experience management company Qualtrics, conducted with 260 in-house marketing professionals, examines the extent to which marketers are working in a silo across many businesses. While integration with sales is lacking, the disconnect with other departments and teams is even more pronounced.

While 54% of marketers regularly share customer insights and feedback with sales, only 50% do so with customer service teams, 29% with the wider workforce, and 27% with the board or C-level executives.

While some marketers may question the need to keep the wider business in touch with customer insights, according to Qualtrics it is only by sharing data that businesses can develop truly effective customer experiences. Leonie Brown, Customer Experience Consultant for Qualtrics EMEA, explains: “Great customer experiences cannot exist in a vacuum. Brands must guarantee that every aspect of the customer journey is delivering a consistent, seamless and high quality experience. To achieve this, everyone involved – from sales staff to delivery drivers to the CEO – must understand how the customer thinks, behaves and what they are looking for.

“Access to data goes a long way to improve each interaction along the customer journey, but information alone isn’t enough. Today the vast amount of data that brands are using look only to the past for insights, reflecting previous shopping habits, purchases and behaviours. By bringing together insights from every customer touchpoint we can unlock “Experience Data” — or X-data — which reveals why consumers behave in the way they do and predicts their next move. That is the real secret to a successful customer experience.”

To find out more about the role of X data in customer experience management, download Qualtrics’ full report here.

Methodology

Qualtrics commissioned a survey with a panel of 260 marketing professionals in July 2017. All respondents worked in-house (rather than agency-side) in UK-based organisations employing at least 50 people and had a minimum of two years’ experience in a marketing role. A second survey of 250 consumers was conducted in August 2017 using the Qualtrics platform.

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Getting Better Analytics And Insights From Your Collected Customer Data

Data collection and analytics are tightly coupled. The mistake we see made over and over again is that companies tend to focus their customer data collection efforts with a single objective (or a single program) in mind. This treats the data collected as a short-term objective, not as a long-term asset. Over time, this results in data islands that eventually “go dark” given that no one is managing customer data as part of an explicit long-term effort.

Have A Long-Term Data Strategy

When it comes to customer data, a long-term data collection strategy almost always proves critical for any advanced analytical work that leads to meaningful business outcomes that can optimize (i.e., simulation management, condition-based maintenance, predictive maintenance and digital twins). Trending analysis, predicting behavior and customer profiling all benefit from long-term data collection strategies. Companies that understand customers’ buying patterns over longer time frames stand to win key insights versus their competitors.

Customer data deserves a data-access-centric strategy to ensure that the data is treated as a reusable asset. This implies that the data should be available to the right people in the company when they need to repurpose it or mine it months or years later. If the data is not findable, threadable (tied to other data sets) or readily accessible, then it’s effectively dark, and its chances of being repurposed are low.

If you are storing your customer data like you store everything else, chances are much of the data you’ve collected from customers has already gone dark. The tendency is to focus on analytical outcomes without preparing the precondition required for the analytics to occur over a longer period of time. If a data strategy for customer information isn’t well-executed, then customer data will reflect the problem you already have in your data center — lots and lots of data sets that represent difficult-to-access data islands.

Thread Your Data

Sophisticated analytical efforts require advanced techniques such as data threading. Threading data across many silos of data is a challenging undertaking. Techniques deployed to achieve threading include (re)ingestion of data, aggregation, parsing, meta data enrichment and indexing. Data is often so extremely siloed that the most efficient first step is simply discovering data islands and recollecting them into an architecture that allows for advanced analytics. The good news is that data capture and storage technologies are relatively cheap, but finding data and then curating it properly does require significant investment.

Customer data needs to be curated and managed as an asset. As more data is collected, it needs to be aggregated with customer data collected during the previous year (or the last campaign, the last payables cycle, etc.).

For example, if a financial institution wants to understand if a customer is approaching a life-changing event such as marriage, having children or purchasing a home, then threading becomes important because it lets you piece together various customer data collection efforts into a single threaded digital dossier. The threaded customer digital dossier allows for different customer data (collected at different points in time) to be accessed for future analytics. It treats customer data as valuable, evergreen and interconnected. A data architecture that allows you to thread and incrementally expand the customer data set is an essential component to making more with your customer data. Advanced analytics, in turn, will allow you make better use of customer data that is properly curated through threading or other data access techniques.

Teamwork

Separating the customer data collection process from the data curation process from data analytics is not a recipe for success. Unfortunately, most companies treat these three activities independent of each other. As a result, customer data is underutilized, undervalued and is not curated as a long-term asset.

The best customer analytics happen when you intersect people who understand the customer data being collected with people who understand how to use and access the data over time. This means that customer data collection efforts need to be discussed in one room with data architecture folks, analytical/data science teams and traditional marketing/customer success teams, ensuring that all have an active voice at the table.

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Smart Master Data Management Will Power Your Customer Analytics And Insights

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It’s supposed to be a post omni-channel world. A time when personalization is the norm, but as customers we know this is not the case. When customers contact companies, the companies often don’t recognize who the customer is, and they certainly don’t know what the customer’s preferences are. The truth is customers rarely get the personalized and tailored experiences they desire.

Well it’s not a shortage of data about those customers.

So why is it that when customers contact companies, most companies seem completely unaware about anything specific regarding that customer, let alone tastes and preferences. The reason is delinquency when it comes to the aggregation and management of the customer’s data inside the company.

Companies have many challenges when it comes to using customer data, particularly data that is supposed to help the company make better decisions in real-time.

David Rowley, CTO of IAC Publishing Labs, and former executive at customer experience software vendor Sprinklr, said, “the notion of a centralized repository for key business data is an important aspect of providing a more comprehensive customer experience.”

But many companies don’t have this today.

This information is called master data. “Master data” can be about products, employees, materials, suppliers but also may include documents and sales. Rowley said, “A centralized master data store (the repository for the master data) can improve real time decision making and analytics, if information about the entities (e.g. customers) is stored centrally. This provides you with a single trustworthy source of truth about the customer.” Rowley added that you can report directly on that data, rather than having to aggregate customer data from separate systems. He said, “A central view of the customer, for example, keeps various systems in sync with what the customer has done.”

Vic Bhagat, CIO of Verizon Business Services, said, “master data is more important now than ever – the amount of data being generated today will pale in comparison to what will be generated in just a few more years.” He added, “Having a master data management strategy clearly defined — early on — enables a faster approach to analytics to deliver a more proactive, predictive, prescriptive outcome that customers expect today.”

As we established, companies struggle with their current state of their customer data management, but what happens when the business becomes a little complicated, such as with a merger or acquisition. How does the company merge all the new data with all the historical data? Master data management can also help with this.

We addressed the fact that many data systems that aren’t integrated. Sometimes data problems include the actual data sources, data duplicates, no data governance and no standards. Data is not always shared efficiently within the organization. Many of the world’s biggest companies operate like separate islands. Customer service and sales do not share a crm. The company does not collaborate around the customer to ensure a powerful customer experience. The company’s various departments operate in silos. More often than not the people working inside the companies do not even know one another in different departments, let alone use data that spans across the organization.

This is a challenge for organizations who need to create easy and elegant experiences for customers. Companies need to leverage data in real-time to make better decisions. Customer analytics are not helpful when one can’t trust the accuracy of the data. And with many companies today data is often too little too late.

“Leveraging Master data enables enterprises to clean, integrate, and supplement their data to ensure a complete 360 view of their customer. With this view, companies can make informed decisions, align different departments, and create world class online and offline customer experiences leading to sales growth,” says Rishi Dave, chief marketing officer at Dun and Bradstreet, a data and analytics company who provides master data.

End-to-end master data management helps clients make marketing campaigns 30% more efficient, improve upsell and cross-sell rates by 60% and increase loyalty members’ spending by 20%, according to Informatica.

Neopost Uses Master Data For A Transformation Initiative

Neopost, a market-leading global provider of mailing solutions, digital communications and shipping services, found that master data was critical for a transformation initiative. The company wanted a way to more competitively know and serve their customers. As Neopost focused on modernizing its offerings and delivery for the digital age, strong data management has played a key role.

“Besides developing the actual software we sell our customers, we’ve got to build an infrastructure where data quality is paramount,” says Steve Rakoczy, Neopost’s North America CIO. “You can make a mess really quickly in the electronic age if you don’t have your data right.” After using master data, Rakoczy said, “We can successfully manage our Salesforce environment for the first time in over a decade.”

Master data management can help your company create more compelling customer experiences, but first the company must decide on a strong data approach.

For more from author Blake Morgan sign up for her weekly customer experience newsletter here.

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