Designing a Well-Received Employee Benefits Package When Cost Cuts Are Necessary

Thanks for joining me for this edition of Pacific on Research. Without further ado, let’s jump in.

Our client, a mid-sized employer, came to us with a question: how should they design and fund their employee benefit offerings to optimize employee satisfaction and retention, and attract new talent? But there was a catch. Due to a changing marketplace, the company needed to cut some costs as well, all while continuing to grow their organization in the face of strict competition.

The company had a mix of benefits offerings, including health plans, a 401k with matching, stock options, sick and vacation time, a flexible savings plan, dental, vision, HSAs, short and long term disability, life insurance, and even discounts with cell phone carriers and other companies. Understanding and separating the cost, value, and impact of all of these programs was daunting for our client.

Many companies, regardless of size and industry, face this same question. Companies don’t operate in a vacuum, competition in every industry forces companies to weigh costs against the value of a strong and loyal workforce. Even companies with high-trajectory growth ultimately face this challenge once revenue growth normalizes, and managing operating costs becomes first priority.

Despite the deep subject matter expertise many HR and Benefits Departments hold about the cost side of benefits, there can be a gap when it comes to identifying how adjusting benefit offerings and levels will be perceived by employees. And employees also feel that disconnect.  They may question how their benefits compare to competitors, how the company arrived at its offerings, and most importantly, they may feel like they do not have a voice in their choices.

To provide our client with information and insights to make clear-eyed decisions with predictable outcomes, we came up with a mixed methodology qualitative/quantitative approach that used traditional marketing research methodologies and analytical techniques in new ways.

We started with an online bulletin board, in which about twenty-five employees from different levels of the company anonymously answered questions posed by our moderator, and were able to see the views of their colleagues and even dialogue with them, over the course of two days. Employees shared, in depth, their:

  • Awareness of which benefits their company offers
  • Understanding of how the different benefits actually work
  • Views of the role that the benefits play in overall job satisfaction, tenure, accepting the job, etc.  
  • Perceptions of how company benefits compare with other employers
  • Prioritization of which benefits are most/least important
  • Willingness to surrender certain benefits, and what their expectations would be in return
  • Process for learning about benefits at the company, and how they want to learn about them
  • Areas of confusion about benefits
  • Wish lists and changes they would like to see

The bulletin board provided us with thoughtful and concrete answers, some out-of-the-box thinking, and a surprisingly consistent and palpable sense of employees’ positives, pain points, and areas of confusion.  It did something else as well. Employees were appreciative of the chance to participate in the process, and felt empowered and vested in the results. Here are a few of many comments we received:

“I really appreciate this opportunity to share our thoughts on our benefits package.  I think this shows a really "classy organization" that tries to get meaningful feedback from their employees.  Hopefully (my company) will be able to take some of these suggestions and mold a benefit package that is used by more of their employees to meet their ever changing needs.  Thank you.”

And this:

“It would be wonderful to be able to see the results from this research and what the company will do with this information.”

Quantitative Phase

Using our qualitative exploration as a springboard, we developed an instrument which included several conjoint trade-off exercises. A trade-off experiment allows us to understand not only the relative importance (also known as share of preference) of each tested benefit, but also what employees are willing to give up or “trade-off” for benefit features that they most value.  While market researchers have been using conjoint methodologies and approaches for years to solve a myriad of business issues from product development, to pricing, to testing which feature attributes actually drive sales, the use of this methodology is often not thought of when it comes to Human Resource business decisions.

In our study, we utilized two trade-off experiments:   

  • One trade-off experiment was designed to analyze how employees choose between medical plans.
  • A second trade-off experiment was administered to understand the importance of individual plan features, and how each individual component of a medical plan contributes to the overall plan selection.

This analysis also allowed us to examine what employees were willing to give up in order to receive the features that were most critical to them.

Among other objectives, we designed this phase to generate the underlying data to create a benefits package simulator. Our clients would be able to adjust benefits in the simulator, and view the results in terms of employee acceptance of various package configurations – to determine optimal configurations at varying overall cost levels. Pretty cool, right?

In addition, there were a lot of great findings brought to light:  

  • Employees at different levels had different perceptions of risk: officers and management felt much more secure in their financial future at the company, and thought benefits were great; other staff had a different perception, and were willing to risk higher long term expenses to avoid short term costs. It’s no surprise that lower compensated staff were more focused on keeping monthly bills down, and more keenly aware of annual increases in rates.
  • Employees had always reported to management (and to HR) that they understood the company’s HSA health plan, which was much less expensive for the company to offer. However, deeper questioning demonstrated fundamental misunderstandings about how the plan worked, and this deterred many employees from choosing it. Here’s a choice quote:

“I don't know how this worked and no one knew how to explain it to me when I was choosing my plan so I went with the other one which is the one everyone understood.”

  • Employees wanted more communication and weren’t getting it.
  • Employees had clear priorities on which benefits were most important, starting with medical insurance and 401k, and clear ideas of which they could reduce or live without.

Not surprisingly, it turns out that employees, like all of us, want it all:  more benefits and lower costs. But a well-designed research trade-off analysis can tease out exactly what is most important and where reductions can be made without impacting overall employee morale and still achieving the needed cost savings for the organization. For our client, this information was gold:

  • The employees initially expressed that they wanted (even demanded) health plan choice, but once they understood it raised overall costs, only a minority continued to say choice was important to them.
  • Employees were willing to pay their own dental premium, receive a reduction in their 401K match from 6 percent to 4 percent, and/or receive a reduction in profit sharing discretionary contribution from the current calculation, if it meant that other benefit levels would stay intact.
  • Employees wanted much more communication about how their benefit package could help them plan for their future and protect their family financially.
  • There was fear and uncertainty surrounding the HSA health plan, centered around a perceived lack of coverage, and a fundamental misunderstanding about the product being inferior or offering less protection for their family.
  • Health plan findings were especially significant:
    • Health plan premium increases of up to 15 percent would be tolerated by employees; providing it was accompanied by a larger company HSA contribution.
    • Monthly premiums accounted for a third of the total decision on which plan to choose.
    • The type of plan accounted for more than a quarter of the decision, followed by deductible.
    • Yearly out of pocket maximums accounted for 14 percent of the decision and co-insurance accounted for only 10 percent of the total decision.

Share of importance (% each benefit has on overall package choice)

Share of importance (% each benefit has on overall package choice)

With the simulator in hand, and clear direction on next steps, our client was able to quickly move to design an effective benefits package that met their cost criteria. At the same time, the company created strategies to tackle areas of employee concern and confusion, and capitalize on areas that employees felt good about. The study also generated employee goodwill towards the company; employees appreciated having a hand in the process.

Well, I hope you’ve enjoyed the read, and maybe generated a few ideas about your own organization.

We’d love to hear your thoughts, so please drop us a line. Or if you’re the meeting type, let us treat you to a cup of coffee and a cruller: info@pacificmarketresearch.com.

To Acquire, Or Not To Acquire

You came, how nice!

In this edition of "Pacific on Research," we discuss our approach to helping a large US company facing a difficult decision. Should they acquire an otherwise attractive company that had recently suffered bad publicity due to claims about their product? Our client needed to understand the impact this move would have on each of the two brands among consumers, distributors, and retailers. And here’s the rest of the story…

Our client, a large US based manufacturer with several successful global brands, was evaluating the purchase of a similarly sized Northern European manufacturer, with a similar product line and better consumer awareness and distribution in certain international markets, including Europe.

As our client saw it, the upside would be access to new markets and distribution channels, access to new customers, consolidation of manufacturing and marketing operations, and greater overall market share. All pretty good reasons. One concern for them: due to a recent safety/product quality scandal, the target company’s image, and value, had been tarnished. (This makes me think of so many other recent company scandals and the range of impacts they had on their brands: Volkswagen, Mylan, BP,  Wells Fargo, etc.)

So…was this a good thing, acquiring a solid brand at a bargain price, or was the company potentially exposing its own brands to harm? And then of course there were the usual questions we see with every acquisition: will I be cannibalizing my own brands, how will this impact our bottom line revenue and profitability, how will consumers perceive the merger, can we effectively merge the two cultures and find operating efficiencies, etc. Even among the largest companies, we’ve seen these decisions made with emotion and gut feelings, seemingly haphazard or incomplete information, overly optimistic assumptions, or in some cases a premature jump straight to price negotiations (if we can get them down to x, let’s buy it, and then we’ll figure out the rest). Faced with decisions with what seems like a firehose of factors to consider, all of us can retreat to a bit of groupthink, choose a few factors to focus on (which may not be the right ones), and move away from a meta-rational approach, dismissing or ignoring data which seems to contradict our perspective.

In this case, we were very fortunate: we were working with a data driven client, and despite the myriad factors and a race to decide, our client wanted a data driven decision.

We collaborated with our client to create a research plan with four phases, to be executed concurrently:

  • Consumers - We set out to establish brand perception of the target brand compared to leading brands and our client’s current holdings. Were there unexploited opportunities for the target brands? A segmentation would help us identify how our client’s customers differed (or didn’t) from the target brand.
  • Observational/Ethnographic Style Mystery Shops – We sent in 75 of our “shoppers” to observe the way the category was merchandised and displayed, to ask questions of salespeople, and to inquire about the various brands - with emphasis on the target brand.  We tested if the scandal was brought up organically, and if not, what was said about it when the “shoppers” inquired.
  • Trade/Retailers, Analysts/Distributors - We conducted in-depth interviews with industry experts and category subject matter evangelists to learn the drivers for purchase decisions in the wholesale market: 
    • What were the levers for shelf space decisions?
    • How was the target brand was perceived in relation to its competitors?
    • Where did the audience see growth opportunities for the target brand?
    • How would they react to the merger of our client and the target brand?
  • Secondary Research - We leveraged existing proprietary and industry research to determine best practices, past successes and opportunities, and delve into successful marketing and communications strategies.

What we found in a short amount of time surprised us, and helped inform our client’s next move:

  • The acquisition would cannibalize one of our client’s main brands.
  • The target brand’s only notable strength rested on an elusive perception that it was stylish and on-trend, which was due to several celebrity endorsements which had been granted free of charge. However, these endorsements were waning and the amount of money required to re-energize the brand in this manner was prohibitive and not in line with our client’s mission or business strategy, which had always been substance over style.
  • Finally, we found that a key distribution channel for our client was adamantly opposed to this particular brand and had pledged to never carry it again, going so far as to train their salespeople on this opposition and regularly telling customers about these issues.  This distribution channel was very important to our client, and our client feared an association with the target brand could impact their existing brands. Ouch!

In short, we advised our client of the significant negatives associated with acquisition, as well as some positives. Our client decided acquisition was the wrong decision.

Their decision was turned out to be the right one moving forward—the damaged brand went on to have their market share shrink by 25%, and while the target brand continues to maintain more of a presence in the areas where it has traditionally been strongest, the brand has flatlined in terms of growth and expansion.

This was a challenging and rewarding project for Pacific. A chance to support our client on an issue of critical importance, and provide them with sufficient insight in hand to make a smart and informed decision.

I hope this post had some value for you, even if it was just a chance to take a break from checking your email. If you’d like to share your feedback, or just say hello, I’m all ears: andrew@pacificmarketresearch.com

Find Your One Thing: Relative Importance Analysis

© Metro-Goldwyn-Mayer Studios Inc/Columbia Pictures All Rights Reserved

© Metro-Goldwyn-Mayer Studios Inc/Columbia Pictures All Rights Reserved

Fans of the 1991 movie City Slickers might remember the crusty old cowboy telling Billy Crystal’s character that if he wanted to find happiness, he should find his “one thing”. In a related tale, our clients often want us to help make their lives easier and frequently ask, "What is the 'one thing' we can do to increase customer loyalty to our brand?" 

Our clients are smart to want to know how to specifically direct their efforts to drive loyalty.  Rather than try to improve their service efforts across the board, they want to make improvements in one or a few areas which will have the most impact on engendering loyalty, thereby increasing sales or improving client retention.  While they shouldn’t ignore poorly performing service areas or let service lag where they are performing well, they do want to get the most bang for their service improvement dollar by concentrating on those service attributes that are most impactful to the bottom line.

Typical survey design sometimes lends itself to methodological concerns about which predictors are actually valid drivers of loyalty. The problem is that predictors are often highly correlated, making the “one thing,” or best predictor, hard to identify. This issue is known as multicollinearity, and generally is best addressed with experimental design using MaxDiff or conjoint where one chooses whether a component is present or absent. However, these design schemes are not always appropriate. As in our example of a survey for the restaurant industry below, service attributes are best measured by degree with rating scales, rather than whether they are present or not (e.g., the restaurant is clean or dirty by some degree as opposed to being clean or not clean).

In 2014, Pacific Market Research conducted a study in the hospitality industry to determine which touch points of service are most impactful or drive overall restaurant loyalty.  In our survey design, restaurant goers were asked to rate the relative importance of 20 attributes of the dining experience in addition to providing ratings on three measures of loyalty to a restaurant establishment. In the resulting analysis, loyalty was the combination of the three loyalty variables and became the dependent variable in our model.

Traditionally, market researchers would look to Stepwise Regression to determine the key drivers of loyalty. Below, one can see the resulting output of the regression model (note - if you are on a mobile device, flip to the landscape mode):

Key Drivers of Total Loyalty

We expect no one is surprised to discover that price and quality of food are the biggest drivers of customer loyalty. However, on their face a few things in this output just don’t make sense. First, it tells us that food portion size and cleanliness of restaurant facility are negative drivers of loyalty. Have you ever heard someone say, “I love going to Joe’s because it is dirty and they don’t give me enough food?” Unless you are a rat on a diet, this doesn’t pass the sniff test (or, in this case, the taste test).

Besides, we know from additional analysis that this is an untrue representation of the data in that portion size is positively correlated with loyalty. Another limitation of Stepwise is we know nothing about 11 of our 20 predictor variables. The model doesn’t tell us anything about location, or cleanliness, or quality of the greeting – which goes against what we know from qualitative feedback to be important aspects of hospitality.  So, while our Stepwise Model isn’t wrong, it is providing results which call the model into question.

These model shortcomings likely arise from multicollinearity between our attributes and some level of model overcorrection. For example, the model struggles to distinguish between attributes that are similar yet distinct such as cleanliness of restaurant and cleanliness of the facility.  In this case, the model chooses only one variable. The model doesn’t need 20 variables to provide an accurate model equation and it doesn’t care about the sniff test; thus variables are left out.

So what are we to do about finding our one thing?

At Pacific Market Research, we often use what is known as the Shapley Value Regression to address these types of issues. The Shapley Value Regression takes all the possible combinations of Linear Regression equations and determines which variables are the most important to the model. Below is an example output (note - if you are on a mobile device, flip to the landscape mode):

Key Drivers of Loyalty

From this chart, we can clearly see that food quality represents 26% of the total importance when determining loyalty – taste is indeed the one thing. Next, it makes sense to concentrate on price and the final farewell in order to drive restaurant loyalty. Greeting, restroom condition and manager’s level of concern are less important factors.

While Shapley Value Regression has provided more plausible and actionable insights, we acknowledge some continued presence of multicollinearity. However, for this example and others, it does the best job of identifying the key drivers of loyalty with rating scale measurements.

Please ask us how we can help you find your one thing to improve loyalty or satisfaction.  We promise not to make you drive cattle to find it. 

Author: Trevor Taylor, Project Specialist/Analyst