How Leveraging Third-Party Analytics Maximizes Efficiencies in Retail, Food, & Hospitality

Utilizing third-party analytics can reduce fraud, shrink and waste in the retail, food, and hospitality industries, and bolster the effectiveness of marketing efforts, with comprehensive data analysis.

July 01, 2021     7 minute read

Food, retail, and hospitality companies hoping to remain competitive must utilize data analytics to inform every decision they make. From employee training programs, inventory shrink, and product distribution, data analytics can strengthen any aspect of business operations.

Notably, analytics made a big pivot in 2020.

Although many businesses utilize in-house data analytics capabilities, it’s not uncommon for internal analysis to remain pretty minimal—missing out on the abundant opportunities that come from leveraging a third-party analytics firm.

In addition to a basic overview of data analytics, read further to learn what insights can be gained from partnering with a data analytics firm that aligns with your company’s needs.

What Are Third-Party Analytics?

Third-party analytics refers to data collection and analysis conducted by an outside firm on behalf of a business to improve efficiencies. This can encompass outsourcing the entirety of its in-house data analytics capabilities—meaning, the outside consultancy monitors real-time metrics and provides comprehensive analysis about operations.

In this case, the first and second parties can be the retailer and customer, or the retailer and employee. The data analytics firm is the third.

With regards to marketing efforts, third-party analytics can also refer to data collected by vendors tracking consumer behavior on websites they don’t actually own. For example, an online outdoors magazine might gather information about which users visit their articles on backpacking, and then sell this to a backpacking company eager to market its own products.

Third-party analytics can also include one company buying information from another about its customers’ purchasing habits. Imagine, for example, a protein bar company purchasing consumer data from a grocery store whose loyalty program database identifies which customers buy other health-related foods.

Both are obviously relevant for a retailer. However, one should prioritize the benefits of hiring a consulting firm to assist with internal operations before venturing into the more expansive territory of marketing analytics.

As we look at the ways to leverage third-party analytics, let’s consider some of the basics about data analytics.

Four Kinds of Data Analytics

We are awash in data these days. Having quality data is key, and that starts with understanding which types exist, including: descriptive, diagnostic, predictive, and prescriptive.

Descriptive analytics inform us what happened in the past. Often in the form of visual graphs and charts, these are utilized to help stakeholders understand how well campaigns and marketing efforts performed—or how efficient a fraud prevention program might be. They provide historical context.

For example, a restaurant seeking to reduce losses or potential fraud, may want to know how many meals or free food a manager is giving out to staff during shifts throughout the past 90 days.

Diagnostic analytics build on this data to explain why something happens. It provides deeper analysis, looking for correlations and potentially root causes.

In the restaurant example mentioned above, a business analyst might identify how much food is being distributed to staff by a manager during the past 90 days. However, that’s not enough to make sense of the situation.

Perhaps reports indicate a particular manager allows an unusually high amount of free food to be consumed during their shifts. At a glance this could be suspicious. However, with further research, the analysis could reveal the manager has fewer available employees to work during their shift, so staff tend to work longer hours or overtime, perhaps deserving more nutritional compensation or rewards.

Predictive analytics use what has happened to forecast or assess what will likely occur in the future. This is done through implementing data, statistical algorithms, and machine learning.

For example, January typically entails a high rate of returned items (many from people seeking refunds on gifts they received during the holidays). A smart business analyst would note this when observing an unusually high rate of returns, which often are flagged for fraud throughout the rest of the year.

Additionally, amid a month with an already high rate of returns, an employee might presume it’s easier to get away with ringing up fake returns or issuing fraudulent gift cards. Advanced shrink analytics should help clarify when fraud is occurring and when it is not.

Prescriptive analytics are a more comprehensive version of predictive analytics. If you have a reliable sense for what future data will bring, you can then determine appropriate actions to capitalize on what you know.

Consider an outdoor gear retailer with a generous return policy: Customers have one year after a purchase to return an item, no questions asked. Just before a new version of a previous rain shell is released, an unusually high number of the previous model are returned by customers for a refund. Learning from this, the retailer extends the period between the release of new and old versions of products going forward, thus minimizing the number of customers likely to treat these older versions like free rentals.

Each of these types of analytics should inform the others. Understanding which kind you need will clarify your operations strategy and help you know when each is appropriate.


Reducing Fraud & Loss Prevention

Minimizing shrink and other related forms of loss are major benefits to leveraging third-party analytics.

Consider a few more fraudulent behaviors that can be monitored and tracked for you by a capable data analytics firm.

  • Unauthorized discounts given to non-eligible customers, sometimes referred to as the “sweetheart discount.”
  • Staff refund goods at full price although they were purchased on discount.
  • Employees use their own rewards account to rack up points from customer purchases.
  • Customers create multiple accounts to receive multiple discounts or special offers.
  • Consumers purchase an item online, reporting a transaction error, and then asking for a chargeback.
  • Buying gift cards with stolen credit cards and then selling those gift cards.
  • Returning funds from a purchase made with a stolen credit card and transferring them to another credit card.

Other Insights Gained From Third-Party Data

Relying only on internal data can leave huge gaps in your company’s analytics. Outside data can enlighten your company regarding shifts in consumer (and employee) behaviors, business trends in your market (or other industries), or relevant news events (such as hurricanes or even riots).

Here are several more ways to leverage third-party analytics.

User experiences can always be improved. For example, you can use third-party data to discover which users have visited NFL-related websites in the past few months. Paired with your internal data, when those same users show up on your website, you can have “Super Bowl Foods” appear as recommended items.

Clarifying your audience becomes easier with external data. By employing third-party analytics to discover other brands your customers like, you can focus on additional companies to partner with rather than investing in less effective partnerships. Alternatively, you can discern what other brands do well and try to learn from them. 

Identifying theft is more difficult when data is siloed. In the case of hospitality, many restaurants keep data separate from the hotel’s analytics, such as guest spending at food services elsewhere on the property. This makes it challenging to pinpoint fraud throughout a resort. It’s also not uncommon for this disconnection to occur within other ancillary areas like pools or amusement parks. The ability to bring all this information together provides a more comprehensive picture for any organization. 

Foot traffic remains a vitally important metric, even with the rise of e-commerce. For example, if you’re a new store on the block, you can find out how much foot traffic nearby shops receive on weekends. Coupled with first-party data, you can utilize this to more accurately forecast how many employees you might need to effectively run your store during busy times.  

Forecasting supply and demand is made better by third-party analytics. If you’re a large food chain, you can use weather forecasts, supplier data, and economic information alongside internal data to better gauge how your dairy prices might be affected by a new bovine illness. Or how much a recent natural disaster, such as a tornado, might delay your delivery schedule.


Choosing the Right Third-Party Analytics Firm

Data analytics can be overwhelming, even for those who have a decent sense for what they’re looking at. You don’t need all the data, just the information connected to your core strategy goals.

But how do you know you can rely on analysis provided by a third party? What usage restrictions does certain data have? And how do you integrate third-party data with your already established internal databases?

Here are a few things to keep in mind as you search for the right analytics firm.

Find an analytics firm that gives you more than data. Sure, you can purchase raw data from third parties or have an analytics firm administer your point-of-sale system (POS), but you’ll likely have the most success with a firm willing to contextualize and analyze all the data for you.

Identify your primary metrics. Again, we are flooded with data. The right analytics firm will help you focus on your most important sources first, rather than trying to upsell you on a bunch of data analytics you don’t need.

Ensure you can trust their expertise. With the rise of big data, many firms have merely added data analytics or data science to their suite of offerings. While this may be an honest aspiration, some companies stretch the limits of how much expertise they actually have in-house.

Review their team profiles and LinkedIn bios. Don’t be bashful about pushing them on their expertise or training. You want to be confident you’ll be getting clean data and prescient analysis. If they can’t effectively explain to you how certain technologies, software, or algorithms work, that might be a good reason to investigate further. Press them for case studies of real clients, not merely hypothetical models.

Be wary of costly or oversized proposals. It doesn’t hurt to start out small when beginning a relationship with a data analytics firm. Guarantee the data you receive is impactful and provides the tangible benefits around your core areas before you commit to more. From there, look for deliberate ways to invest further into a third-party analytics strategy. As the adage goes, “Fail small and fail often.”

Set clear boundaries that affirm your values. Emphasize transparency and honesty, between you and the vendor, as well as you and your customers. Demonstrating to consumers how much you value transparency and consent regarding their data can go a long way in building brand affinity—and that starts with making sure your values are clearly stated to a potential data analytics vendor.

So whether your company needs to make internal processes more efficient or wants more robust analytics capabilities, a third-party consulting firm can be a godsend.

The Zellman Group is a premier comprehensive loss prevention solution including expert data analytics and more. With decades of experience in retail, food, and hospitality, we work together with our clients to establish long-lasting relationships built on trust. We can help you identify operational inefficiencies, sales opportunities, and training gaps as you become a more data-informed business. Contact us today to learn more.

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