On the heels of Google’s success with Product Listing Ads and Shopping Campaigns, other publishers have developed their own product ad platforms, most notably Bing with its Product Ads and Facebook’s Dynamic Product Ads. These major players have proven that shopping ads are a viable and highly effective marketing investment for digital advertisers.
And, shopping ad campaigns have now been around long enough for us to identify exactly which trends retail advertisers should be most aware of.
In 2014, advertisers spent a whopping 318% more on product ads in December than they did in January of the same year. Share of clicks closely mirrored this spend, with one in four paid-search clicks being on a product ad in December of 2014.
It’s undeniable that this trend will continue, as spend continues to increase, product ads get more sophisticated, and advertisers continually optimize for the most aesthetically pleasing and persuasive ads.
Again, the holidays are always a boon for digital advertisers in the retail space. During Q4 of the last couple of years, spend on shopping ads pulled ahead of text ads, since retailers served engaging and eye-catching ads during that time. What’s surprising, however, is that this year, text ads have an advantage.
Since mid-year 2014, text ad CTR has actually increased, and overtook shopping ad CTR until the end of the year.
Not only are we experiencing a mobile revolution – there is a groundswell of mobile ad clicks. Even though desktops had a 25% increase in clicks during November and December of 2014, this pales in comparison to smartphone’s almost 90% increase between January and December. During the holidays, consumers are now shopping and clicking at the same time, on screens uniquely designed to offer ads, deals, and product information to someone on the go.
For more information and data charts on Google and Bing shopping ad 2014 performance, download our new report, Google and Bing Shopping Ads Report: Current Trends and What Lies Ahead.
With Google processing more than 3.5 billion search queries in a single day, there’s a surprising amount of insight that can be gained by analyzing the content and user behaviors behind these searches. Studying your own paid search data can substantially benefit several areas of your marketing strategy, including product, pricing, competitive strategy, branding, and store location selection.
Search engines periodically release insights based on overarching or vertical-specific search trends. Bing and Google both offer places to start researching search trends related to your product. This data can help you:
If you analyze search query data directly associated with your brand name, you can identify customer pain points, bugs, and potential new product features.
SEM ad creative testing can help inform pricing strategy, as well as the best way to message pricing.
The search engine results page is a goldmine of competitive data. By performing searches related to your product, you can:
Even if you don’t plan to launch a competitor campaign, it’s important to monitor your own branded search terms. The simplest way to do this is to look for spikes in your brand CPC. If it jumps, you may have a new competitor.
Search data can help you test new approaches to brand messaging. Marketers can utilize search data to:
Search data can help inform your store location strategy by providing:
Search marketing isn’t just a direct response channel, but rather a means to inform your marketing strategy with data. Product, pricing, competitive strategy, branding, and store location selection are just a few examples of how to use search data. Make it a regular practice to analyze search data for insights that can be applied throughout your marketing strategy.
Sarah manages Content Marketing at Boost Media and leads a team of marketing professionals to drive revenue through complex B2B marketing campaigns in the ad tech industry. Prior to joining Boost, Sarah developed marketing and sales strategy at BNY Mellon, a top 10 private wealth management firm. In a former life, Sarah worked in journalism writing for magazines including Boston Magazine, The Improper Bostonian, and Luxury Travel. When she’s not writing engaging content, Sarah enjoys cooking, running, and yoga.
Boost Media increases advertiser profitability by using a combination of humans and a proprietary software platform to drive increased ad relevance at scale. The Boost marketplace comprises over 1,000 expert copywriters and image optimizers who compete to provide a diverse array of perspectives. Boost’s proprietary software identifies opportunities for creative optimization and drives performance using a combination of workflow tools and algorithms. Headquartered in San Francisco, the Boost Media optimization platform provides fresh, performance-driven creative in 12 localized languages worldwide.
Click here to schedule a free demo of the Creative Optimization platform today.
As a performance marketer, there’s never a day when I’m not using an analytics platform to measure the performance of my campaigns. It’s extremely important to track and measure advertising spend and understand how people are engaging with the assets you’re promoting. If you’re looking for a way to prove the effectiveness of your marketing efforts, then you’re in the right place. In this post, I’ll walk you through how to set up and define goals and conversions, tag your advertising campaigns for consistency, and analyze the data for actionable insight on Google Analytics.
Goals are essential, allowing us to see conversions, conversion rates, and conversion values. From making a purchase to downloading a whitepaper or requesting a demo, tracking goals is critical in evaluating the quality and value of traffic to your website. It’s important to measure different segments, understand how people are engaging with your site and content, and see what the outcomes are.
Now that you have goal tracking figured out, the next step is to tag all of your advertising efforts with consistent landing page parameters in order to get a clear view of what channels are most effective. For a basic understanding of how this works, please refer to Google Analytics’ URL builder. There is no absolute way to do this and it depends on how you want to structure your advertising for ease of reporting, but once you develop the model, it is important that you stick with it. Here’s how I apply the utm parameters in order to understand performance for Marin.
No matter what you decide to put in these parameters, it is very important that you use the same naming convention throughout. If you don’t have a clear understanding of how to use utm parameters consistently, it will get very messy and be very difficult to understand your data.
Example Scenario: I need to promote our 2015 Mobile Report on various channels – Facebook Ads, Twitter Ads, and Linkedin Ads. This is how I would setup the landing page utm tags per channel based on the type of advertising that’s running.
Do you see the consistency? The campaigns are all labeled the same naming convention, 2015Mobile Report, for all channels. The source and medium are the same labels that I’ve used throughout time for all marketing initiatives. For the content parameter, I use the ad name if the channel relies on a banner, otherwise I use it to evaluate groupings of my target audience.
Let’s do a quick dive and put these consistent tracking parameters into action by analyzing the data using the tags indicated, in addition to looking at goal completions to see how many people converted.
Example Analysis: Taking a look at each source/medium for the 2015 Mobile Report campaign, I can see that most of the website traffic and conversions are coming from facebookwca and linkedin-su. I can back into the cost per conversion by getting the advertising spend. So, say that I spent a $1,000 on facebookwca and $1,000 on linkedin-su, the cost per conversion in this scenario would be $16.13 ($1000/62) for facebookwca and $17.24 ($1000/58) for linkedin-su. As for the other campaigns, I expected the low results because they are new campaigns that were recently set live.
To do a high-level source/medium analysis, navigate to: Acquisition > All Traffic > Source/Medium.
To do a high-level content analysis, navigate to: Acquisition > All Traffic > Source/Medium, then click on “Other”, expand “Acquisition” and click on “Ad Content”:
The steps for analyzing both are similar to the steps indicated in the campaign analysis – you simply type in the parameters you used in your URLs and your data will populate.
If you have any questions, feel free to leave a comment. By spending time analyzing your marketing campaigns, you are one step closer to becoming a performance marketer.
In the previous post we discussed how to segment your customer base for lifetime value (CLV). In our fourth and final post of this lifetime value series, we’ll provide some recommendations for optimizing for CLV.
Once you have identified audience segments, you’ll want to investigate things like:
The idea of optimizing for CLV is to achieve increasing profits from your existing customer base, or find new high-value customers.
Be channel agnostic when looking for marketing opportunities
When asking these questions, it’s useful to take a channel agnostic approach to your thinking. Instead of relying on data from one specific channel, you’ll want to incorporate data signals across different marketing channels like search intent, behavioral data, and audience characteristics, in addition to your own data, in order to paint a detailed picture of your high value segments and develop strategies to increase your customer CLV.
Once you have a good understanding of your high-value customers, you can start developing strategies to drive higher CLV. A few potential scenarios follow below:
Scenario 1 – Find new high CLV customers
A financial company could improve its acquisition efforts by refocusing its spend towards an acquisition channel that has proven to attract high CLV customers. Alternatively, it could also expand its reach by leveraging 2nd and 3rd party data to create look-alike audiences for additional targeting opportunities.
Scenario 2 – Reduce customer churn
An insurance company creates an audience segment for current customers with contracts expiring within the next 30 days in order to run a search retargeting campaign that ensures the brand has top that the brand is well represented when the user conduct relevant searches.
Scenario 3 – Increase repurchase rates
An online clothing retailer rolling out a new seasonal line could remarket its product ads on Facebook to customers who haven’t bought from the company within the past 6 months.
Scenario 4 – Cross-promote or upsell to potential customers
A home improvement retailer could look for opportunities to cross-sell to potential customers who’ve recently shown interest in home loans. Alternatively, the retailer could promote upsell opportunities to sell kitchen upgrades to customers who’ve recently indicated interest in or purchased a new appliance.
The possibilities are numerous. By taking basic customer lifetime value information, developing useful segments, and connecting that information with the customer data that you’ve collected or have access to, you can open up a number of new, interesting ways to make smarter marketing decisions and increase profits.
Looking for additional posts in this series?
3 Reasons Why Segmentation is Necessary to Understand Customer Lifetime Value
Gathering Customer Lifetime Value Data – Start Small and Build
Using Lifetime Value to Create Audience Segments
Now that you have your data in order, you’ll want to start parceling out relevant customer segments.
The goal is to identify segments that are practical and helps you make better marketing decisions. You’ll want to strike a balance between segments granular enough to execute targeting strategies, but broad enough to scale your targeted marketing efficiently.
There’s no shortage of advanced statistical models to determine customer segments, but in this post we’ll provide a few straightforward, practical recommendations.
Start segmenting based on customer value.
The 80/20 rule, which states that 80% of your profitability comes from 20% of your customers, is a good place to start your segmentation effort. You might actually find your customer profitability distribution is even more extreme with over 100% of your profits coming from your top customers, and your worst customers actually taking away from your bottom line. However, at the most basic level, the distribution might look like the above graph.
In this case, we’ve identified high CLV, medium CLV and low CLV segments. However, these segments are still too broad for much use. From here, you’ll need to rely on your data to help unearth demographic or behavioral commonalities within each segment.
Ideally, you’ll want to try to identify segments with commonalities that you can act upon and measure changes in performance.
For example, you might find that:
Once you’ve identified different customer segments, you can identify opportunities to optimize for your higher CLV segments. We’ll explore some ideas to optimize for lifetime value in our next post.
It’s important to segment your customer data to gain useful insights, but the relevant segments may not become apparent until you start wading through the financial data. In this post, we’ll discuss three important financial factors you’ll need for your customer lifetime value (CLV) calculations.
There are three important things you’ll need to find:
1. Retention rate/Customer lifetime
2. Revenue per customer segment
3. Margins per customer segment/sale
The latter two are pretty straightforward, and don’t leave much room for interpretation, so in this post we’ll focus on divining the retention rate. (One caveat on revenue – businesses with a brick and-mortar presence should also make sure to account for the “research online, buy offline” effect when calculating CLV. Otherwise,customers acquired via online channels will be undervalued.)
Customer “lifetime” doesn’t mean forever
“How long is a customer’s lifetime?” is a pretty common question. The “lifetime” in lifetime value isn’t quite so literal. You’re not expected to be projecting profits for the 75 years or so of a customer’s life. For one, that’s impractical. But more pressingly, the further out the revenue projections, the less reliable they are. Generally, we would recommend calculating your CLV within a five-year window (although some exceptions apply).
There are a number of different ways to figure out your customer’s lifetime. Two of the more common methods involve using your retention rate or your repeat purchase rate.
Scenario 1: Use retention rate
If you charge your customers a monthly or yearly retainer or subscription fee, you should know your monthly or yearly churn rates (retention rate = 1 – churn rate).
For example, an enterprise SaaS company with a yearly retention rate of 90% would have an expected customer lifetime of 10 years based on the equation below:
Scenario 2: Use repeat purchase rate
Repeat purchase rates are very similar to retention rates, except they are more relevant for companies whose customers have irregular purchase cycles. For example, a retailer with repeat purchase rates of 50% will have different customer lifetimes depending on whether their customer buys from them every 3 months, or every 3 years.
Scenario 3: You know neither
Some businesses may not know what their repeat purchase or retention rates are. In those cases, you’ll have to back into the number. You can do this by specifying a gap of inactivity where you decide a customer is no longer a customer, and then measuring the time period between the customer’s first and last purchase.
The period of inactivity in which you determine a customer has lapsed depends on the nature of your business. For example, a grocery store whose best customers buy weekly might determine that a 3-month dry spell is sufficient enough to indicate that they are no longer customers. Other businesses – auto dealerships for example – will have much longer lag between purchases.
Don’t forget about your segments
Regardless of how you calculate your customer’s lifetime, remember from our previous post that you’ll want to find the customer lifetime for each relevant segment you’re measuring, not just the total average customer lifetime.
This is part of a 4-part blog series. Missed the first post? Read 3 Reasons Why Segmentation is Necessary to Understand Customer Lifetime Value here. Our next post will dive deeper into how you can carve out relevant customer segments, so stay tuned!
Marketing metrics like CPC or CPA only capture the cost part of the profit equation. This seems a bit odd when you think about how often successful marketing campaigns are judged on the revenue portion of the equation. It’s also detrimental over the long-term because it treats your marketing efforts as a cost-center vs. a revenue center.
Incorporating customer lifetime value (CLV) is important because it takes both revenue and costs into account. Our recent white paper provided an introduction on how to introduce customer lifetime value into your online marketing campaigns. This post is the first of a 4-part series that will provide pragmatic recommendations for building lifetime value models.
Prelude – Get your data ready
A lifetime value model is only as good as the quality of the data you keep. To develop a good CLV model, you’ll need to ensure you’re accurately measuring things like revenue per customer, margin per sale, and retention/churn or repeat purchase rates.
Once you have your data, there are three good reasons why you need to segment your customers to get an accurate understanding of your customer lifetime value.
Reason 1: Average Revenue Per User (ARPU) is incomplete
A simple example can illustrate. Let’s say a business has two customers:
Customer #1 is an unmarried, 25+ y/o librarian, $35K HHI, and buys a hatchback for $15K. Customer #2 is a married, 55 y/o executive with 3 kids, $250K HHI, and bought a sports car for $160K.
Based on this data, the average customer for this business buys $80K cars. Except in reality that $80K car is far outside the price range of Customer #1, and it might not be upscale enough for Customer #2. Furthermore, the average customer’s demographic info based on the details above is basically worthless.
This is why segmentation matters. ARPU tells you that you have paying customers, segmentation can tell you who’s actually driving your business.
Reason 2: Focus your effort on the segments matter most
Go back to the two customers above. Which segment appears to be more valuable to you? Which is the one you want to focus your marketing on?
That’s a trick question. The answer is it depends. If you can convert the hatchback buyer and she buys several cars from you over her lifetime, then she could be worth more than the sportscar buyer. But if you were focusing on a window of 3-5 years, then you’d probably want to focus your marketing efforts on other sportscar buyers.
Either way, you can’t make this decision until you know how different your customers are.
Reason 3: Make better decisions
You might choose to target both customers. And that’s where segmenting for CLVcan be most helpful. Once you can divulge a segment’s CLV, opportunities for acquisition, upsell, and cross-promotion may become more visible. For example, you might leverage retargeting campaigns to upsell to segments with high CLV. Alternatively, you may find that there are cross-promotion opportunities with a segment with lower CLV but potential for growth. Finally, you might decide to cut marketing in channels that consistently acquire customers with low CLV.
Ultimately, understanding your customers’ CLV enable you to be a better marketer. This is a 4-part blog series, so stay tuned for more best practices to follow! [Part 2: Gathering Customer Lifetime Value Data – Start Small and Build]
It’s no secret that, due to the measurability of online advertising, marketers now have to face an avalanche of marketing data. To turn all this rich data into valuable insights which can then drive revenue, you have two options:
Hiring an army of analysts to manually digest data is an expensive and resource intensive approach, so unsurprisingly most marketers are choosing to go down the technology route. This way they can use technology to digest their data and analysts to think more strategically, adding an important layer of human insight.
This shift towards technology is evidenced by a recent Gartner statistic which claims that by 2017 the CMO will spend more on technology than the CIO. This highlights that marketers are now beginning to build out technology architectures to cope with their avalanche of marketing data. However, most CMOs and marketers in general have very little experience with technology implementations. We thought we’d share a few best practices to help marketers who are building out their technology architecture, based on our experience of hundreds of marketing technology implementations for enterprise class advertisers around the world:
1) Don’t hoard data, do something with it. I spoke a lot about this in my previous post “How to Avoid Being a Data Squirrel.” The key is to get the correct analytics, attribution and optimisation layers in your technology architecture and distribute the results across the enterprise. This way you can ensure you’re turning your analytics and attribution data into actions that optimise revenue outcomes.
2) Your technology layers don’t all need the same logo. Just because you wear Nike trainers, doesn’t mean you drink Nike beer, use Nike cleaning products or use a Nike tablet. Focus on finding best of breed technologies in each area of analytics, attribution and optimisation, then integrate them. The enterprise technology space is increasingly open, and Salesforce.com pioneered this by bossing the CRM space, whilst integrating data from best-of-breed technologies in other areas through the App Exchange.
3) Ensure technology saves your team time. Ultimately, if technology doesn’t save you time then you may as well just hire an army of analysts. Look for technology which integrates data that would otherwise be done manually, creates more efficient workflows, and makes the management and optimisation of campaigns more efficient.
4) Look for third-party technology. When it comes to the layer of technology responsible for attributing media spend, it’s important to look for third-party technologies independent from media owners. If you allow a media owner’s technology to attribute spend then there are potential issues of bias involved towards their own media channels. In essence, it’s a bit like letting an estate agent decide how much you’re going to pay to buy a house.
5) Optimise to revenue, not just leads. Revenue is what drives your business and increasingly marketers are becoming accountable for revenue. When building your architecture ensure that your technologies are optimising towards bottom-line revenue for your business, not just form fills, quotes or other softer lead targets. This can be achieved by integrating marketing programs with CRM or ERP data, offline conversions over the phone and in-store or integration with proprietary data in other areas of your business. This bottom-line marketing optimisation is what will satisfy your CEO and CFO that the marketing technology investment is worthwhile.
We will be talking in more depth about these tips and best practices to help marketers build effective technology architectures at a number of events across Europe before the end of this year. So if you’re going to be at Ecommerce Paris, iStrategy London, Search Congress Amsterdam, Ecounsultancy JUMP, SMX Stockholm or our own Marin Masters conference in London at the beginning of November then be sure to join the conversation in person. Otherwise, feel free to join the conversation below.
With the amount of data now available to digital marketers, they’re in danger of becoming data squirrels that hoard data but do nothing with it. There aren’t enough analysts in the world or hours in the day to manually analyse all the available data, and crucially, turn it into actions which optimise revenue outcomes.
The modern day digital marketer needs to consider how to turn all the data they have into actions which optimise revenue outcomes through advertising technology. To do this they need to understand the three layers of advertising technology:
What you don’t measure, you can’t manage. Analytics platforms gave marketers the tools they needed to finally end the age-old problem of not knowing which half of their advertising is working and which half isn’t. Measurement is crucial, and it’s exactly why digital marketing has had the sensational growth rate it has.
However, whilst analytics is vital, when in a silo it can’t help marketers understand how and when customers interact with multiple channels. Furthermore, it doesn’t interpret the data into campaign adjustments which optimise revenue outcomes.
Whilst analytics platforms have gathered data for marketers, the attribution technology layer is what helps them make sense of that data. Attribution has been a hot topic for marketers over the last few years as they’ve looked to understand their consumers’ path-to-conversion, and how to better allocate marketing budget and tailor messaging as a result.
However, whilst attribution platforms make recommendations on how to better allocate spend, they don’t implement these changes. For example, attribution platforms do not translate their output into bid recommendations for particular keywords on search engines or banners on display networks.
The final layer of digital marketing technology is the optimisation layer. These technologies take the data from the previous two layers and turn them into actions that optimise revenue outcomes for the marketer. They drive incremental performance gains and time savings.
However, these platforms would struggle to do anything without the data provided by the previous two layers of digital marketing technology. Data is the lifeblood of the marketing technology architecture, and without it the key final layer of optimisation couldn’t happen.
It’s worth considering that not every layer in digital marketing technology needs the same label on it. It’s a bit like choosing shampoo and conditioner; just because you prefer shampoo from Brand A doesn’t mean you can’t buy conditioner from Brand B if you prefer. The same applies to marketing technology, focus on finding the best technology in each layer. Today’s enterprise technology world is open and connected, best-of-breed technologies can facilitate data flow between themselves seamlessly.
For many search marketers, identifying opportunities for optimization within paid search campaigns is challenging. Monitoring and maintaining top performing ad groups, keywords, and ads is a standard best practice; but as campaigns grow, keyword lists expand, and creative tests multiply, this approach fails to scale and provide incremental improvements in paid search performance. With so many optimization opportunities hidden in an ocean of data, how can search marketers give the required attention each campaign deserves? Where do you even start?
To help search marketers answer these questions, Marin Software is thrilled to announce our partnership with BoostCTR to offer a free paid search diagnostics tool that not only provides insight into account performance, but also opportunities for optimization. The Account Performance Grader is designed to analyze historical performance across keywords, ads, quality scores, and ad groups for AdWords and Bing Ads campaigns. Simply sign up and enter the required information to receive your customized report.
Among other best practice recommendations, this report will provide actionable insights for pausing poor performing keywords and ads, as well as reveal quality score trends that identify areas where keyword relevance can be improved. With the Account Performance Grader, search marketers can remove the guesswork out of campaign optimization and focus their time on more strategic, high impact tasks.
Sign up here and start optimizing your campaigns today!