How to Identify if Yelp is a Good Marketing Channel for Your Business

Businesses must employ comprehensive conversion tracking technologies to measure the impact of marketing channels like Yelp. These tools enable marketers to monitor and attribute customer actions, such as form submissions, phone calls, and chat interactions, to specific marketing initiatives. By collecting data from various touchpoints, conversion tracking provides a focused view of the customer journey and helps identify effective channels for direct conversions. We first established these tracking technologies shortly after taking over the marketing management for this home improvement business. 

At the time, Yelp was already a channel in use, though the business didn't have a way to properly track results with call tracking using unique calling numbers placed on the external website. As you can surmise, that number didn't carry over to the website if the user clicked to learn more about the business outside of Yelp. So, we selected a new tracking system that allowed for dynamic number swapping, which changes the number on the website for any visitor coming from that source. That way, the users you click through from Yelp will still see the same Yelp tracking number. However, we also improve the accuracy of the data using several other technologies to gain visibility into not just the converting data point but the entire customer journey.

Marketing automation platforms play a vital role in conversion tracking by streamlining the process of capturing and analyzing customer interactions. These tools allow businesses to track user behavior across multiple channels, including email campaigns, landing pages, and social media. By integrating marketing automation with conversion tracking, companies can gain valuable performance insights into marketing efforts to make informed decisions when optimizing marketing.

Marketing automation tools are great, especially when they provide multi-touch attribution modeling to show you all touch points in a customer's journey. Even if the system itself can't do this, you can export the customer histories to rebuild patterns in customer journeys across multiple contacts.

Form submissions are a critical conversion point for many businesses, as they often represent a user's intent to engage further with the company. Form tracking technologies enable marketers to monitor and attribute form completions to specific marketing channels. By analyzing form submission data, businesses can identify which channels are most effective at generating leads and driving conversions. 

Your marketing automation platform should include form tracking since those platforms are often where you build the forms for your site. Just remember to set up your UTMs and your site to allow persistent UTM values to ensure your analytics platforms present you with the most accurate information across page views.

The first step is to calculate the mean or average ROI across all years in the dataset. This average provides a baseline to understand the central tendency of the client's return on investment from Yelp over time. The mean is calculated by summing up all the yearly ROI values and dividing by the number of years.

Mean ROI = (Year 1 ROI + Year 2 ROI + ... + Year 10 ROI) / 10

A Z-score can be determined for the most recent year's ROI with the mean and standard deviation calculated. The Z-score measures how many standard deviations an individual data point is from the mean. It allows for an "apples-to-apples" comparison of data points from different normal distributions.

The Z-score formula is:

Z = (X - μ) / σ

Where:

X = The most recent year's ROI value

μ = The mean of the years' ROI values 

σ = The standard deviation of the years' ROI values

Finally, based on past Yelp results, the Z-score can be converted into a probability to determine the statistical likelihood of our client achieving their goal ROI. You use the Z-score to understand how many standard deviations you are away from your intended goal, which provides the likelihood of achieving those results when comparing current status against goal ROI. With the Z-score and the possibility of achieving the intended ROI calculated, we can be confident in how likely Yelp's more recent results will produce the expected results we saw in the past.

We started with this probability to quantify the risk of continuing to invest in Yelp. Comparing the client's required ROI to achieve their goals against the likelihood of attaining that ROI better informs the decision to continue or stop investing in the platform. However, when you have larger datasets like we do, different periods may show distinct patterns in results. So, no matter how much data you have to find trends, it's also essential to compare that to the most recent data.

While analyzing the full multi-year dataset provides a long-term perspective, emphasis should be placed on the most recent several years' performance, especially if a declining trend has been observed. Focusing on recent data better reflected the current dynamics of the client's Yelp results. Comparing the mean, standard deviation, and Z-scores of the entire multi-year period versus just the last 3 years quantifies how much the client's ROI has deviated from their historical average.

By rigorously analyzing the client's historical ROI data and emphasizing recent results, we statistically substantiated a recommendation to pause the Yelp investment. These decision-making models demonstrate the power of data-driven analysis to optimize marketing channel allocations and improve overall ROI. However, it's not the only calculation necessary to consider for these vital marketing decisions.

Channel mix modeling is a powerful tool for optimizing marketing budget allocation across various channels. It uses statistical techniques to quantify each channel's contribution to overall sales or conversions, considering their interactions and synergies. By analyzing the historical performance data of different marketing channels, businesses can determine the optimal mix that maximizes their return on investment. This data-driven approach helps marketers decide where to allocate their budget for the greatest impact.

When a channel like Yelp consistently underperforms, it can drag down the overall marketing ROI. Channel mix modeling can quantify this impact and help determine if the resources allocated to Yelp would be better spent on other channels that deliver more substantial results. For example, if Yelp is driving a low volume of leads at a high cost per acquisition, it may be more effective to reallocate that budget to channels with a proven track record of delivering high-quality leads at a lower price.

In the case of the home improvement client, channel mix modeling would analyze Yelp's contribution to key metrics like lead volume, cost per lead, and revenue in comparison to other channels like Google Ads, Social Ads, and email marketing. This analysis would quantify Yelp's overall influence on marketing performance. If Yelp is found to have a negative impact, such as driving up the average cost per lead without a corresponding increase in lead quality or revenue, it strengthens the case for reducing or eliminating investment in the platform.

For the home improvement client, multi-touch attribution modeling could reveal that while social media advertising may not be the final touchpoint before conversion, it influences brand awareness and nurturing leads in the top and middle funnel of the customer journey. A customer may first discover the brand through a Facebook ad, then visit the website to learn more before ultimately converting through a Google search ad or by calling the business directly. Multi-touch attribution modeling would assign credit to the Facebook ad for assisting in the conversion, even though it wasn't the final touchpoint.

Like social media, we wanted to check if Yelp played a role in a customer's journey other than the conversion alone. Additional channel touchpoints could influence our final decision by gaining a greater understanding of the full impact of the marketing channel. 

Multi-touch attribution can also uncover correlations and synergies between marketing channels. For example, our home improvement client has found that increasing their investment in social media advertising leads to a corresponding increase in branded search volume and direct website traffic.

This insight would suggest that social media is effectively building brand awareness and driving customers to seek out the business through other channels. Optimizing the social media campaigns could then indirectly improve the performance of the search and other channels as well.

By leveraging multi-touch attribution modeling, our firm can paint a more complete picture of each channel's contribution to the client's overall marketing success. This holistic view informs strategic decisions about where to allocate the budget for maximum impact.

As a home improvement business specializing in windows and doors, the client has a lower purchase frequency and longer sales cycle than other industries. Unless a customer moves to a new location in the business' service area or buys a second home nearby, purchases may only need these services every few decades, which limits the potential for repeat business in the short term.

Additionally, the high ticket price of these projects means that customers will likely spend more time researching and comparing options before deciding. This extended consideration phase can make it more challenging to attribute conversions to specific marketing touchpoints, and regular engagement and multi-touch marketing attribution are required to keep the marketing tracking accurate.

In contrast, businesses with shorter sales cycles and lower price points, such as restaurants or retail stores, may see a higher return on investment from platforms like Yelp. Customers are more likely to make impulse purchases or rely on reviews for these businesses, making Yelp a valuable source of leads and revenue. For the home improvement client, the lower purchase frequency and longer sales cycle make generating a positive ROI from Yelp more challenging.

Due to the longer time between installations in most customers' cases, the lifetime value of a customer from Yelp is nearly 1:1 with the revenue generated from the 1st job they compete with our client's company. Again, that makes our assessment easier because we know that the marketing channel needs to drive enough revenue to maintain a significant ROI. Remember that even though we're primarily focused on marketing costs, our client still incurs a considerable cost in servicing these installation projects. Therefore, if profitable growth is the revenue goal, Yelp alone had to create a meaningful multiple of incoming revenue against our monthly advertising spend (when offset to account for lead-to-sale timelines of several months on average). 

By considering the unique characteristics of the home improvement industry and the client's business model, we provide a more nuanced recommendation on whether Yelp is a good fit for its marketing mix. While Yelp may be effective for businesses with high repeat purchase rates and lower price points, it may not be the most efficient use of resources for a company with a longer sales cycle and higher ticket prices.

In conclusion, our firm's in-depth analysis of Yelp as a marketing channel for this home improvement client demonstrates the importance of data-driven decision-making in optimizing marketing performance. By leveraging advanced conversion tracking technologies and rigorous statistical analysis, our firm was able to quantify Yelp's historical ROI and determine the likelihood of achieving future success on the platform. The declining ROI trend in recent years and the client's unique business characteristics, such as long sales cycles and low repeat purchase rates, ultimately led to the recommendation to discontinue investment in Yelp.

Our firm's use of channel mix modeling and multi-touch attribution further underscored the importance of evaluating each marketing channel's contribution to overall performance. By understanding the interplay between channels and the specific role each one plays in the customer journey, businesses can make better decisions about allocating marketing budgets for maximum impact.

However, it's crucial to recognize that this recommendation is specific to one home improvement client and may not apply to all businesses. The effectiveness of Yelp as a marketing channel depends on several factors, including industry dynamics, target audience preference for that platform, and the business's unique value proposition. Therefore, you must run similar data gathering and analysis programs for your company to ensure you're making the right decision for your marketing dollars.

For businesses with shorter sales cycles, lower price points, and higher repeat purchase rates, Yelp may be a valuable source of leads and revenue. The platform's emphasis on customer reviews and local search may be effective for restaurants, personal care, retail stores, and other small businesses. The key takeaway is that businesses must continually evaluate their marketing channels based on individual revenue goals and performance data. What works for one company may not work for another, and what was effective in the past may not continue to deliver results in the future, as we saw with Yelp's changing ROI.

By adopting a data-driven approach to marketing optimization, businesses can stay agile and adapt their strategies to changing market conditions and consumer behaviors. This data-driven approach requires a commitment to tracking and analyzing key metrics, testing new channels and tactics, and making informed decisions based on empirical evidence. In the case of the home improvement client, our decision to move away from Yelp is a testament to the power of this approach. By leveraging data and advanced analytics, we could identify a channel no longer delivering sufficient ROI and recommend reallocating resources to more effective channels.