Bidding to value isn’t new. In fact, a lot of advertisers have been doing it for many years. Where an advertiser is supplying revenue data directly to the platform, such as revenue from a tag or linked ecommerce data from Google Analytics, value bidding is already taking place. However, for businesses with more complex or longer sales cycles, or driving multiple channels of interaction with customers, understanding value can be an arduous and complex task.

Use value-based bidding to maximize ROI.

In a nutshell, when you use bid strategies in your media buying platforms, the main difference between a Target CPA (cost per acquisition) and a Target ROAS (return on ad spend) bidding strategy is that while Target CPA adjusts your campaign bids to help you meet a predefined cost per conversion goal, Target ROAS adjusts bids to help you maximize the value of conversions you’re receiving as a result of your advertising, and thus focuses on ROI. 

For Google Ads and the new Search Ads 360 in particular, Google has been clear about the fact that CPA bidding or bidding for conversions is limiting the ability of bidding algorithms to eke out performance, as you are assuming that all customers that interact with ads are bringing in the same business value. 

However, we all know that this is not the case. Customers come in all shapes and sizes; some will take longer to make decisions to purchase or interact with your business, some are going to be customers interested in smaller purchases, while others still will be looking at larger purchases or longer sales cycles. This can also become even more complex when customer touchpoints move from online to offline, such as an outbound call center. 

It wouldn’t make much sense to bid for all of these customers with the same value logic. By focusing on segments of customers based on the value they would bring to us, we can maximize our return on our ad spend. This is especially true for B2B or subscription businesses, where not all prospective clients are equal. 

The complexity of value-based bidding only needs to be as complex as you need it to be for your business, but the level and complexity of the data you are sending to your performance platform will provide you with much more robust reporting metrics, and more data for bidding algorithms to get things done.

As we can see, as we move up the complexity of our bidding goal, moving away from clicks/conversions to value and then profit, we need to supply the platform with less proxy metrics, and more revenue and value data. At the most mature stage, the ultimate goal for businesses is to send customer lifetime value data to the platforms to enable automated bidding and to predict future customer buying behavior based on their previous purchasing patterns.

Test and set up value-based bidding using proxy metrics.

For direct sales and subscription businesses, value-based bidding would of course involve simply passing back the value of the sale or rolling subscription back to the platform as an offline conversion, for example in Campaign Manager or Google Ads. However, if your marketing is targeted towards lead generation and longer sales cycles, bidding for value becomes slightly more complex, requiring the use of proxy value metrics. 

For example, let’s say that you have four stages within a typical sales journey, all trackable via conversion tags or Google Analytics, or perhaps via integration with CRM as an offline conversion. It could look like this:

Lead Submitted (25%) → Marketing Qualified Lead (20%) → Sales Qualified Lead (15%) → Closed Deal 

We need to work backwards from the Closed Deal value, to assign a value to a Lead submission:

Closed Deal $1000 → SQL $150 → MQL $30→  Lead Submitted $7.50

Given that a Closed Deal is worth $1000 in this example, we divide each subsequent stage by the prior stage conversion rate.

We can now understand the value of the first conversion point in the customer sales cycle and assign a value to the lead submission, then perhaps do the same for other conversion points on your site (for example, phone calls or “contact us” forms). These values can then be assigned to our bid strategies to assign the real value of customers to your business. Remember, machine learning is only as useful as the information that is being supplied to it!

Once you have values assigned to conversion points, you can use features such as Custom Columns in Search Ads 360 or Google Ads to add these values for your automated ROAS bid strategies, then let the platform algorithm do all the hard work with this new information. 

Look ahead to predicted lifetime value.

Of course, the ultimate goal we should seek with bidding in performance media is to add more of a predictive value to our target, so that the bid strategy is able to bid on keywords that are likely to drive longer lifetime value, rather than one-off purchases, short-term subscribers or low value B2B customers. This can be done by adding predictive intelligence to our bidding platform, and involves integration of CRM with a data platform and machine learning tool, such as Google BigQuery and BQML. 

You can then export these predicted values to your platform of choice as offline conversion data, and point the bid strategy at this particular goal to maximize, which in this case predicts lifetime value. This is where we think all marketers should aspire to be and plan towards, and it’s something we bring up often with clients as an important horizon goal to have with the future of their first-party data. 

Customer value-based bidding, combined with media platforms bidding algorithms, will help you monitor the real impact of advertising on your business and make the right decisions to develop growth strategies, ultimately allowing you to capture the customers that generate the most value, and those that matter most. Again, the data you share with platform algorithms is a crucial factor in competitive success, and unlocking insights related to value will prove crucial to brands looking to improve performance within an intensely competitive digital landscape.

Media.Monks will take on media strategy, planning and buying, and measurement for Unity globally. With subject matter expertise in gaming, VR, Web3 and the metaverse, Media.Monks’ integrated team will scale up media to engage Unity’s core gaming business and its B2B audience.

“Media.Monks is the right fit for our business given our shared expertise and belief in how RT3D, the metaverse and the next phase of the internet are changing not only gaming but many other industries,” said Carol Carpenter, CMO, Unity. “We are excited to partner with them to unify our media efforts globally, and work together to deliver unique solutions for customers.”

In addition to the media AOR assignment, the Media.Monks creative development teams use Unity software to deliver real-time 3D solutions for clients across a wide range of industries. Recently, the Unity technology powered Media.Monks’ development of an award-winning AR experience, ‘Anne Frank House: The Bookcase for Tolerance,’ honored at the Cannes Lions Festival of Creativity in the Digital Craft category, and many more including The Webby Awards, The One Show, ADC Global and D&AD.

Learn more about the work Unity and Media.Monks are doing to build successful B2C brands in the metaverse by tuning in to an on demand discussion between Unity’s VP of Accelerate Solutions, Ryan Peterson, and Media.Monks' SVP, Tim Dillon. Tim will discuss insider lessons and insights gained from working with major consumer brands––from getting started in the metaverse, ways to leverage a real-time 3D game engine to making a genuine impact, and more. Listen now.

This review was led by Tenx4, an agency search consultancy who specializes in helping Global B2B Brands identify the right agency partner. “We’re on a mission to fix the broken agency RFP process to be about ‘the fit’ rather than ‘the win’ and it is clear that the partnership between Unity and Media.Monks is the perfect fit,” said Ashley Cohen Chandler, Partner, Tenx4.

How is cost per click calculated?

The amount you spend for a digital click is broadly determined by four factors:

• Competition: more advertisers mean you are directly competing with more bidders in the auction.
• Quality Score: taking into account the quality of your ad and how relevant your ad message and landing page is to the search query.
• Bidding strategy: your maximum bid you are willing to spend per click or impression.
• Seasonality: when you advertise also has an impact on your cost per click. For example, if Black Friday is a popular time for competitors bidding, this will mean you are competing with more advertisers in the auction.

In Google Ads, you specify your maximum bid for keywords or phrases, then Google evaluates your ad to determine its quality score. Your quality score and maximum bid together make your Ad Rank. Google will use this Ad Rank to compare you to other advertisers. This is important so that only relevant and good quality ads are shown at the top of search results, a benefit to both the user and the advertiser.

Google then works backwards to answer the question: what is the bid the advertiser with the highest Ad Rank needs to win the auction? This is where the formula above comes from. The highest you will ever pay is your maximum bid, though advertisers can still win the auction with the lowest maximum bid by having the highest relevance.

How has cost per click changed over the years?

“Cost Inflation” is a term often sprouted by digital marketers as they worry about the increasing costs of running paid advertisements across platforms such as Google and Facebook. Whether that be pay-per-click search campaigns, image-based shopping ads or display campaigns on social media and popular blog sites, we are seeing a very similar trend: it is becoming more expensive to advertise. 

A study by Hochman Consultants looks at the average cost per clicks across 50 advertisers on the Google AdWords Network from 2005 to 2019, where the advertisers came from a range of industries. We can see that cost per click has been growing steadily over time, and in 14 years, the average cost per click is almost triple that of 2005. This study was conducted in the US, but we are seeing a similar trend across advertisers in the UK.

Like any competitive market, pricing is determined by supply and demand. When Google Ads first launched in 2000, there were 18 million searches per day worldwide. In 2019, there are around 5.6 billion searches a day worldwide—the equivalence of 63,000 searches every second. Not only is that 300x the search volume of when Google Ads first began; with growing capabilities of targeting, you are really able to whittle down which search queries you want to bid for based on location and for which demographic you want your ad to shown to. Also, the number of advertisers in digital marketing platforms are increasing, and spend for UK Paid Search media surpassed that of TV in 2015 becoming the most spent channel. 

How does cost per click differ across social media channels?

Facebook and Twitter are the cheapest to advertise in, with Instagram being double the cost. This is mainly due to a different ad format mix, where Instagram ads are more likely to be videos. LinkedIn is by far the most expensive to advertise in, as they offer a very niche audience of professionals, with filtering options to target job title, years of experience and company name for example.  

How can I lower my cost per click?

As mentioned earlier, cost per click factors in the quality of your ad in the Quality Score metric. In the example above, the advertiser willing to spend the least actually won the auction due to having a high-quality score. So how can you improve your quality score and therefore lower your cost per click?

• For Google Ads, ensure your ad is relevant to the keyword you are bidding for. If you are bidding for “hand sanitizer” but you really sell washing powder, you may need to rethink your keyword strategy.

• Make sure that your landing page is relevant for the ad. For example, a landing page with 100 different items to shop from including hand sanitizer wouldn’t work as well as a landing page that is only about hand sanitizer, because Google will deem the latter more relevant

• Optimize for click-through-rate. This is often the case with social media advertisement as well as Google Ads. Having a clear call-to-action and a good creative will help.

The final note is that historic performance of your account is taken into consideration when calculating cost per clicks, so take the time to make sure all ads are optimized as much as possible. With the info above, you should be well prepared to optimize toward success. Learn more about how we can help you now.

Just in time for putting the finishing touches on strategic marketing plans for the new year, Forrester has recently released three reports, authored by Principal Analyst Jay Pattisall, identifying key goals for modern marketers and the partners who can best help achieve them: the Q4 2021 reports “Now Tech: Media Management Services,” “Now Tech: Global Marketing Services” and “Now Tech: Marketing Creative and Content Services.”

When read together, they illustrate a marketing services industry in flux—a moment that puts the traditional advertising/marketing services model into question. Jay Pattisall writes in Now Tech: Global Marketing Services, Q4 2021: “CMOs and B2C marketing executives, if you, like so many we have spoken with, wrestle to deliver more growth and acquisition with fewer marketing resources, then it’s time for a new model. No longer will you find the effectiveness needed to elevate your marketing impact by piecing together multiple best-in-class providers from market to market.” We couldn't agree more.

It's Time for Agencies to Answer the Call

Pattisall's insight pre-empted a recent article by Jameson Fleming at Adweek that captures agencies and digital service partners wrestling with an existential question: what's their superpower? Exploring how agencies can better demonstrate their value—particularly toward marketers who are dealing with the very same urgent needs as raised by Pattisall's research—Fleming advises agencies “to show how different moving parts can function together to create more agile and responsive teams.”

Taking the opportunity to share our own perspective, S4Capital Founder and Executive Chairman Sir Martin Sorrell explained the strength that lies in our digital-first focus: “The great thing about digital is it’s about brainpower, not brawn,” he said. “When you’re buying media in a nanosecond, the need for brainpower rather than brawn becomes more and more important.” This inseparable relationship between strategy and agile execution is crucial to unlocking the flexibility and speed that brands require to adapt and prepare for another year of disruption.

This means partners who can offer creative problem-solving supported by a customer-centric perspective when it comes to content; who can deliver growth within the stop-and-go pandemic recovery in their global marketing; and deliver expertise in media management to respond to shifting privacy regulations imposed by both governments and big tech—not to mention consumers’ own evolving digital behaviors.

The Industry is Overdue for a New Model

Flip through the names listed in the reports linked above and you'll find that Media.Monks is the only name that shows up on each—because we've always understood the necessity of integrating and unifying each of these capabilities into a new model built for a new era. Even when specialist partners belong to the same holding company, foundational complexities inherent to the traditional model often stifle their ability to coordinate swiftly together to meet a client’s need. “The irony of networks is they do not have a network effect,” Media.Monks Co-Founder Wesley ter Haar told audiences at Advertising Week New York last fall, detailing how our API model is built to scale up and flex out across solutions and capabilities at speed.

The same idea translates to our merger model. Noting that many traditional holding companies rely on mergers and acquisitions to scale capabilities and delivery, in the Forrester report noted above, Pattisall advises marketers to “ensure all the operating companies share the same goals, the same incentive structure, and answer to the same leader—ideally the global account lead.” This is where true integration thrives in building alignment. 

“For traditional companies, the old trade was you retain your autonomy and independence and we’ll do the back office. Our trade is different: you merge your company into ours, and you become part of Media.Monks,” Sir Martin Sorrell told Erik Siekmann in a recent episode of the Marketing Transformation Podcast. This approach to mergers—not acquisitions—lays the groundwork for streamlined collaboration across clients’ integrated marketing needs, because everyone is on the same team united by the same values.

Toward Stronger, More Flexible Partnership

The need for greater flexibility has become only more urgent as global supply chain issues, inflation, pandemic uncertainties and other geopolitical disruptions continue. This has led to an urgent need for scalable solutions that fit brands’ changing needs as digital behaviors evolve. One way we’ve been able to build strong, collaborative relationships with brands is our “land and expand” strategy, in which we’re brought in to solve a key need, then work along with our clients to zero in on more opportunities to make a greater impact or deliver stronger results. 

In “Now Tech: Marketing Creative and Content Services, Q4 2021,” Pattisall recommends setting “an environment for co-innovation” between yourself and partners: “Increase your brand’s and your agencies’ chances to hit the creative mark with a co-innovation partnership, where internal marketing experts and external content and campaign experts work together.” With a shared desire with both parties to innovate, our “land and expand” relationships with brands grow along with their ambition. In addition, this frees brands from a continual cycle of navigating competitive pitches that slow innovation momentum.

Such an approach only works when a partner has broken down the silos and barriers that traditionally stifle collaboration, innovation and agility—for example, marrying strategy, data with production to boost creative effectiveness in real time. By collaborating with a partner whose unitary model provides seamless access to a diverse team of global talent, brands will be better supported in strengthening the effectiveness of their marketing across the digital ecosystem.

This subject becomes all the more pertinent in the current economic climate where budgets are being cut and these cuts need to be made where the impact on the business is minimized. 

The most common case that central teams are faced with in setting budgets is the feedback loop, a multi-stage process where the individual markets are asked how much they need. This inevitably ends up in a circular process that goes something like this: 

• Round one: Central media director/CFO tasks markets to come back with a detailed plan on media requirements to hit targets (two month’s duration).
• Round two: After each market has submitted their budgets, central media director and CFO ‘level’ the budget—i.e. overlay a reality check on each market while also shaving an amount off all markets collective estimated budget to align with the actual global/European budget (one month). 
• Round three: Budgets are submitted back to local markets, where they have a chance to re-justify their budgets with fact-based proof of why they would need incremental monies (one month). 
• Round four: Final stage of ‘levelling’ by the central team and budgets are given to the markets (half a month).
• Round five: Potential repeat(s) of rounds three and four (further months).

This process works effectively if three assumptions hold: local markets know how media affects their targets (sales/profit); all markets use the same logic, assumptions and data sources; and no market over-estimates in an attempt to raise a higher local budget. 

Now, clearly, any national marketing director worth their salt will build a business case that will over-estimate what they actually need in order to get the budget they want. Hence those markets that have either strong political might, advanced data modelling and knowledge skills, or just good sources of data, will more than likely come out of the process with more budget at the expense of other markets. The issue is that these markets are not necessarily the markets that represent the most efficient use of funds to achieve the highest overall sales/profit. 

One answer to all this could be to turn the process on its head and enforce budgets by market centrally. This removes the political and local market influence, but such a big step (if not already in place) may create ill-feeling among national marketing directors. 

A more favorable option is to allocate the budgets centrally using a framework that the local markets can input into, based on a common process with common assumptions and data sources. To do this, the framework needs to be easy enough for all to understand, complex enough to incorporate all business elements, and use similar data sources across markets. 

This framework can be used centrally to allocate and justify budgets and the onus is on the national marketing managers to justify why they should have altered budgets, but only when given the facts. This minimizes political arguments, reduces time (rounds one, two and five are now obsolete) and arrives at a more optimal split of money, ensuring markets are supported where return is greatest. 

In my experience of working with international companies on market and brand portfolio allocation projects, there is a wide range of control that the center has over the individual markets. As such, the success of the framework is highly dependent on the engagement process between the center, the partnering consultancy and the local countries. Where there is a greater degree of local control, the process will take longer and needs to capture more visible inclusion of local market understanding.

The framework can consist of a range of methods, starting off with the bronze approach structured around key performance and cost metrics, up to the gold standard all-singing all-dancing optimization and forecasting software. 

The bronze approach is a good starting point. This gathers key metrics such as sales, profit, consideration metrics, cost of media, media inflation etc., and creates overall scores based on these metrics. This score, or index, then offers insight as to which markets are more attractive to invest in. 

However, it doesn’t bring to bear key points for investment, such as how much to spend in those markets (due to its linear approach) nor effectiveness of advertising in those markets. As such, this bronze approach may be unacceptable for companies where there is a lower central control level; the approach is more likely to result in opposition by the local markets due to its lack of analytical rigor versus alternatives. 

Market Mix Modelling (MMM) is a great tool to use in the first step to budget setting and allocation. However, it is only a first, but necessary, step: the ROI is not only informed by the effectiveness of the advertising, but also how much you are spending. Dixon and Shapiro spell this out, where they cite that forecasting using MMM gave a forecast error of 13% compared with the more detailed, integrated approach which had only a 3% error range. This detailed approach included understanding of rapidly changing environments, long-term impacts and finally reach, or the advertising response curve.

The response curve is a crucial factor in setting budgets. In Figure 1, if the client’s TV budget was at the current spending point, and MMM was used to measure the effectiveness, it would correctly measure the return and hence ability to calculate the ROI (in our example, £2 sales for £3 spend, £2/£3 = £0.66). However, if the client was actually to spend a reduced amount (one-third of the amount), and this spend returned only one half the amount of sales, then the ROI has increased (£1 sales for £1 spend,£1/£1 = £1.00), as the proportionate amount lost in sales is less than the decrease in spend. 

The reason this relationship of changing ROI exists at different budget levels is the diminishing returns effect of advertising. This is where, at higher spend levels, the efficiency of a medium starts to diminish as you begin to reach the same consumers again and again, and there’s also a finite level of demand for a product. 

This diminishing returns effect means that if the initial MMM ROI were to be taken in isolation, then the TV medium in our example may not be an attractive investment opportunity. However, it shouldn’t be ignored in investment decisions because at lower spend levels it is an attractive medium. This application is critical in optimizing across channels and also across markets. 

Obviously, there isn’t historical data to build response curves in all markets and for all channels, but benchmarking and intuitive client discussions can get around this. 

So the gold standard framework will build in the measured activity from MMM, and add further texture through building response curves. Future proofing the results is the next step to make sure the model has applicability in further years. This includes factors that historical data may not exist for, such as future economic trends, new product launches and new media creatives. These can be controlled for use in estimates and benchmarking against comparable events, such as a rival launching a similar product. 

The next step is to include long-term impacts. Where the effect can’t be measured, academic research can help inform the tail—of which there is some very good literature. One of the landmark studies being the Lodish behavior scan studies, “A Summary of Fifty-Five In-Market Experimental Estimates of the Long-Term Effect of TV Advertising.” 

The last step is then overlaying real-world factors and constraints: minimum spend thresholds for channels (cut-through) as well as pre-committed budgets (such as three-year sponsorship deals). 

While building these curves using consistent datasets across markets, you can then give the opportunity for local markets to feed back into the process a further time. This gives the chance to contest relationships, build in these real-world constraints and include new data—but only if it is truly justified. 

Building these curves into a tool gives the ability to trade-off millions of possible combinations to arrive at optimal allocations for given scenarios. 

Once built, this model enables the exploration of financial impacts of different investment decisions, including: altering the investment allocation between individual markets, sub-brands and media; changing the focus of the communications mix (total media versus total promotions) for the key investment targets across markets and the portfolio; and increasing and decreasing the total level of investment as well as the optimum budget scale for sales and profit maximization.

Regarding the latter point on optimum budget scale, this is a key benefit of the process. In Figure 2, the analytical framework has highlighted that there is no profit upside in increasing budgets beyond currently planned levels: the points on the graph are sales and profit simulations of changing the overall marketing budget by increments of £1million. 

It is, therefore, possible to increase gross sales value (GSV), but it would be at a cost to profit. However, there is a significant benefit in better allocating the current budget across brands and across media channels by moving from the black point to the turquoise point on the graph. 

This approach can thus be used by the chief marketing officer/chief financial officer to frame budget discussions based on real data. So when asked the question of cutting budgets and minimizing business impact, they can make the right decisions backed up with financial proof. 

In practice, this altering budget happens throughout the year, so having something in place to simulate these changes and illustrate the reasons why is a massive time-saver. 

In times of reduced budgets, local market politics and the need for increasing accountability, it’s important to have a budget process that everyone buys into, not only across markets but also across disciplines. In the interest of minimizing business impacts of reduced budgets, transparency is key and building a budgeting framework can go a long way to iron out the pains of the process. 

Delivering corporate accountability and ROI improvements will help put marketing further up the agenda. Optimizing the marketing mix on a single brand typically delivers ROI increases of 15% or more; further optimizing across a portfolio of brands, and then across markets, the overall ROI improvement internationally is huge. Learn more about how we can help now.