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Resources: Articles, Case Studies, Power BI, Wiki, Client Success Stories, Whitepapers and Marketing Insights from the Roivenue team.

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Resources: Articles, Case Studies, Client Success Stories, Whitepapers and Marketing Insights from the Roivenue team.

180% Increase In ROI From Affiliates (TESTS)

Jun 9, 2020 10:44:04 AM / by Pavel Šíma posted in Case Studies

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AXA Assistance identified the good affiliate publishers from the bad with a custom attribution solution:

"Separating affiliate partners who bring real value from those who are only cannibalizing your marketing budget can be a nightmare. Especially if you want to compare their real ROI, not only among themselves but also to other performance channels, and take the whole customer journey into account."

 

The Challenge

Everybody who uses the services of affiliate partners ponders their worth. Often, they can leach on the performance of other channels by showing ads to customers who are very likely to make a conversion regardless of the affiliate ad.

An ideal affiliate partner will bring new customers to your website without any help from the other channels. Such publishers deserve their commission fee.

However, if the publisher steps into an already existing customer conversion paths, it deserves less credit. But how much exactly?

To make matters worse: data from the affiliates are often served in bulk, hard to work with formats. The client had preformed a previous analysis that revealed a deeper look into the issue would be needed in order to produce a publisher evaluation. At this point, it became clear: a data-driven attribution based solution would be helpful, as it could account for the entire customer conversion path.

 

This is a scenario perfect for ROIVENUE's attribution analysis, a feature which is built in by default. Unfortunately, there was a complication. There wasn't enough conversion paths related to the affiliates, which means that the built-in algorithm would not have enough data to make its calculation reliably.

This did not stop us. To solve this problem for our client, we designed a custom solution, which calculates and presents the data in a different fashion.

 

 

Solution

Roivenue's attribution solution was used to capture customer paths across the whole marketing mix. All touchpoints and their costs were automatically loaded into the system, transformed and exported for further analysis.

 

As a next step, we analyzed the mapped journeys and divided all paths that included an affiliate touch point into three categories:

 

1. AFFIL ONLY

All touch points in the path are related to an affiliate – this is optimal.

 

Path 1 

 

2. AFFIL THEN PAID 

An affiliate has attracted the customer, but the customer later came into contact with other paid campaigns. This situation is sub-optimal.

It is valuable that the affiliate caught the customer for the first time but the fact that there has been contact with other paid channels signals that the affiliate was not strong enough to close the customer by itself. This increases the cost of the path as other non-affiliate channels needed to take action.

 

Path 2 
 

3. PAID THEN AFFIL 

An affiliate has entered a conversion path after paid channels. In this case, the affiliate’s value to the whole marketing mix is highly questionable and it is correct to assume that this affiliate publisher cannibalizes on other paid channels in the mix.

Path 3

 

In order to be able to divide customer paths into the three described categories, we first had to map these paths.

 

This is where ROIVENUE's attribution measurement comes into play, as it was designed for the reconstruction of paths and data driven attribution modelling.

 

All traffic is divided into channels of two types:

  • Traffic from affiliate partners
  • Traffic from other channels – paid channels (such as AdWords or Facebook) and unpaid channels (such as organic and direct).

All of these other channels – both paid and unpaid – fall into the paid channels group in this article.

 

Our next step was to gather data for one month, to collect enough customer paths for the algorithms to produce reliable results – the more paths we collect here, the higher is the precision of the evaluation that follows.

 

After the data gathering period, the collected material was exported to Power BI for final analysis.

 

              

 

 

Above you can see an overview of active affiliate publishers and division of paths related to them based on the description above.

 

Once we have the paths for each affiliate counted we can preform a simple calculation to see the tendency of path 3. This index represents a reversed value of the division of count of path 3 to the sum of counts of paths type 1 & 2. The Publisher Quality Index (PQI) for publisher x is defined as follows:

The higher the value of this index, the lower a publisher’s tendency towards paths of type 3 and thus the higher is it's perceived quality.

Publishers are evaluated and ordered by total paths related to them in the table below. By default, this dashboard considers both conversion and non-conversion paths but using a slicer, we can specific one or the other.

It is finally possible to compare publishers to one another and decide if cooperation should be retained or stopped.

 

Now, without criteria through which to judge, the above numbers are meaningless. Our suggestion is that, like in school, a 0.6 is the cutoff point, as below this value signals a strong tendency towards path 3. It is also important to view value of the index for each publisher with accounting for the number of paths related to the publisher – the more paths, the higher reliability of the index.

 

After the initial setup of data collection in ROIVENUE, this analysis can take place periodically, so the behavior of the retained and new publishers can be continuously monitored and the quality of the affiliate portfolio can be maintained.

 


 

Results and Takeaways 

AXA had been struggling with evaluating the quality of different affiliate publishers for years. 

ROIVENUE offered a customized analysis utilizing it's expertise in attribution.

As a result, AXA saw an increase in ROI of its affiliate program as well as it's entire marketing mix.

 

 

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Attribution case study

Sep 28, 2019 10:19:34 PM / by Pavel Šíma posted in Case Studies

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Connecting Marketing Data and CRM with Roivenue Data Integration

Jul 23, 2019 11:17:11 AM / by Sample HubSpot User posted in Case Studies

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When it comes to tracking your business performance, you don’t have to limit yourself to your internal database. Smart marketers know that there is a lot to be gained from integrating all of their 1st party data from external marketing systems into one place. Roivenue helps them connect the worlds of marketing and business into one seamless experience.

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Achieving 120% YOY Growth

Jul 22, 2019 2:00:07 PM / by Sample HubSpot User posted in Case Studies

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“Over just a quarter we were able to grow our marketing  profit by tenfold on one of our largest E-Commerce projects. On the same revenue by managing our budgets WEEKLY with ROIVENUE. “

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Customer Acquisition Up 36% On The Same Marketing Spend

Jul 22, 2019 1:24:54 PM / by Sample HubSpot User posted in Case Studies

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To reach incredible growth you need high-quality, accurate data you can rely on. Roivenue automatically processes data from all marketing channels and e-shops and facilitates (makes simpler) important decisions.

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Orange Doubles Revenue Thanks To Ad Impression Attribution

Jul 19, 2019 2:02:30 PM / by Sample HubSpot User posted in Case Studies

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Orange Telcom and Digiline Double Revenue From Brand Campaigns While Retaining OAS Thanks To Impression Level Attribution

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Slevomat Rides The ROIVENUE™ Wave To Growth

Jul 19, 2019 1:20:00 PM / by Sample HubSpot User posted in Case Studies

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ROIVENUE™, a marketing multi touch data attribution platform, was tasked to help increase revenue of Slevomat while keeping the overall marketing budget at the same level.

________________

Slevomat is the #1 experiences site deal site on the Czech and Slovak market with over 2 million satisfied clients.

Currently, Slevomat is leading the way by using a data-driven attribution approach to marketing channels optimization. For a company always looking to edge the competition and spend money wisely Slevomat challenged ROIVENUE™ with this goal and the platform delivered incredible return on investment of over 19% within key marketing channels.

 

         The Process

 

By analyzing the current performance of Slevomat’s digital marketing mix, the ROIVENUE™ platform was able to quickly:

 

1. Break down all incoming traffic into 45 meaningful manageable channels​

 

2. Analyze ROI performance of said channels based on data-driven attribution models

 

3. Identify performance and propose budget reallocation to channels with higher ROI

 

This allowed the ROIVENUE™ team to analyze the campaign’s limit saturation, and find opportunities for growth using ROIVENUE’s Budget Optimizer feature. The optimization process was to reallocate budget from low performing channels to those identified by the ROIVENUE™ attribution suite as high performing channels.

 

         Results

 

Using ROIVENUE™ Budget Optimizer the Client Success Team recommended a two wave course of action.

 

During the first wave, the data collected suggested that Slevomat increase their spend on Facebook and Adwords by only 0.20%. Basically the overall budget wasn’t changed, but reshuffling budgets between channels resulted in revenue increase by +19.67% and ROI increase by +19.41%.

 

During the second wave, the ROIVENUE™ Budget Optimizer aggregated the marketing attribution data to suggest a focus of marketing efforts on the Criteo platform.The ROIVENUE™ Client Success team suggested to split Criteo ROI measurement from one overall channel to three distinct channels within then traditional funnel: Upper, Middle, and Lower level.​

 

This allowed Slevomat to use ROIVENUE’s multi-touch attribution models to optimize budget spending precisely in the section of the funnel that would give them the greatest marketing ROI.

 

By redistributing budget from more saturated parts of the funnel to the lesser saturated ones, (the ones with higher ROI according to data driven attribution), overall ROI of the platform went up.This time, budget was slightly decreased, and even with that and proper budget split the overall performance resulted in revenue increase by 5.92% and marketing ROI boost by 12.64%.​

 

“Optimizing only selected channels with ROIVENUE™ during the initial phase assured us that data driven approach is the right way. Moreover, the level of ROIVENUE’s support was just phenomenal.”

 

David Matoušek, ​CMO Slevomat

 

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Acquiring And Retaining Better Clients

Jul 19, 2019 10:43:33 AM / by Sample HubSpot User posted in Case Studies

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“The biggest success of the past year is our very low percentage of defaulting clients. We expected this number to be around 25%, but, thanks to this great system, we reached an incredibly low 15% client default rate. It is really unique in the short-term loan segment. Continuous evaluation of marketing investment let us effectively work with marketing agencies, especially during the initial new client acquisition phase. Roivenue gave us an overview of marketing investment efficiency, and we made very precise, accurate decisions.”

      Edita Zvařičová, CEO, KOUZELNÁ PŮJČKA s.r.o.

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How to achieve growth, lower client acquisition costs, and decrease the number of defaulting clients? Czech loan provider Kouzelná Půjčka (translated: Magical Loan) faced all three of these challenges. We succeeded in improving all three parameters, thanks to a deep analysis of total customer value, conversion paths, and ad optimization using Roivenue.

 

How to achieve growth, lower client acquisition costs, and decrease the number of defaulting clients? Czech loan provider Kouzelná Půjčka (translated: Magical Loan) faced all three of these challenges. We succeeded in improving all three parameters, thanks to a deep analysis of total customer value, conversion paths, and ad optimization using Roivenue.

 

Problem: High Acquisition Costs And Defaulting Clients

Magical Loan offers non-bank, short-term loans. As with any loan provider, Magical loan needs to analyze every loan application in order to minimize the possibility of client default. To do this, all applicants fill out a complex questionnaire for scoring purposes. This questionnaire has a high abandon rate – meaning people open the form but do not fully complete it – which deters many lendees. Magical Loan set up a very strict scoring system to curb the number of defaulted loans, making it necessary to optimize marketing investment. The idea was to optimize relative to loans granted, not in relation to submitted loan requests.

 

Challenge: Optimizing Acquisition Costs Per Predicted Loan-Seekers’ Success

We expected that a certain number of loan-seekers, which were targeted with ads, would start filling the questionnaire. Out of those who finish the form, 70% of the loan requests will be refused outright, and 25% of loans granted will default. If we were to succeed in moving those percentages (conversion rates), it would translate into a significant lowering of the marketing costs associated with getting the initial requests and reduce the burden posed by defaults.

 

Our Solution: Optimization Of Ad Investment Relative To The Probability Of Loan Repayment

We used the Roivenue Performance Monitor to analyze the cost of every conversion step and measure the performance of the different conversion stages. We then created prediction models to measure the effectiveness of marketing channels from the overall view.

 

Roivenue can evaluate cost of marketing investment relative to Customer Lifetime Value (CLV), and differentiate between new customer acquisition and retention of existing customers. This simplifies the optimization of campaigns to a very basic level: invest more in effective channels and lower investment in problematic ones. The next step was to analyze the actual form-filling process – in real time.

 

We used the data-mining algorithm in R to calculate the probability of a given user’s progress on filling out the loan – uncovering several mistakes in form code and possibilities for improvement of the UX. Most importantly, we were able to classify different types of  applicants, predict the level of loss in relation to segments, and discover problematic (potential) clients. This included certain patterns that were in fact attempts at fraud.

Result: Stable Growth And Extremely Low Level Of Loan Defaults

The continuous improvement of performance parameters lead to stable growth and an extremely low level of defaulting clients – just 15%. Thanks to several revisions of the form based on Roivenue data, we succeeded in raising the conversion rate for those who filled the form to 36%, and lowering the cost for new client acquisition to 30% CLV.

 

 

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