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Use the full potential of your customer data!

Turn customer data into customer insight

The Refining Smart Data process supports businesses through a variety of data-based applications. The workflow supports business challenges in 9 steps from the initial goal-setting to implementation and the final model and method checks. 

As business goes digital, companies are gaining increasing amounts of data from a wide variety of sources. But in order for it to be analysed and processed, these data need to become accessible in a centralised way and linked up. 

 

Modeling is made easier by methods of machine learning (also referred to as artificial intelligence) as well as automated A/B tests performed in thousands. 

These models may assist the monitoring and management of marketing campaigns. Their results enable a qualitative assessement of the models as well as increase the efficiency of the individual channels, instruments and activities.


The newly-gained insights are fed back from the campaign model into the data centre. This is how the Refining Smart Data process may be applied or restarted at any time, allowing to continuously optimise your database and increase your productivity.

The benefits of working with Corvendor:

The purified data from internal and external sources become your new intelligence centre. 

New technologies integration allows you to identify, shape and support the most relevant campaign for each customer in the most suitable way. 

Valuable information and forecasts about the purchasing behaviour of your current or prospective customers becomes easily available. 

 

The collaboration between your marketing and sales teams as well as your partners becomes optimised through data-based, automated processes

Georg Zedlacher

Chief of Staff Marketing 
EMEA

Georg Zedlacher

Marketingleiter
Dell EMC

We have commissioned Corvendor to build an EMEA online sales system - the main component of our strategic lead generation framework at Dell Technologies in the EMEA. Today, we can testify that our results have been excellent. The Corvendor system enables us to forward leads from our IT projects to Sales in a structured way. It gives us support in both our general project planning as well as business management. It allows us to segment and thus accurately predict the number of leads and the marketing pipeline.


Due to a lack of own resources and experience in this particular area, we entrusted Corvendor to provide their expertise. Their approach is very structured and, significantly, cross-departmental, which positively affected our results. After 6 months of working together, we calibrated our current status an subsequently implemented the developed processes into our existing infrastructure.

We can highly recommend working with Corvendor, especially in the area of data-based decision-making.

The Refining Smart Data Workflow

Targeted, data-driven campaigns in 9 steps

Use Cases

Find out by checking our case studies how we can practically support your business, and how Refining Smart Data can make your operations easier and more successful.

Use Case: General

Step 1

Business Case

Let us have a look at the case of an international car manufacturer - a company with decades of history and detailed information about their existing and new customers. Refining Smart Data in this case entails an in-depth analysis of the available data to improve the communication and the customer journey. The car manufacturer typically wishes to: 


a) Acquire new customers cost-efficiently from their competitors 


b) Identify existing customers about to “jump ship” and prevent them from doing so 


c) Recognise the customers’ buying goals, e.g. a buying a specific model of a vehicle 

Step 2

Raw data

In case when millions of existing and prospective customers have already interacted with the company, the CRM and the sales data can provide a lot of valuable information that the company can leverage. 


The sales person knows that…

a) Mr. Moore purchases a second-hand car once in three years. 

b) Ms. Smith leases a new convertible once in two years. 


The marketing department at the headquarters knows that… 

a) Mr Moore has already visited the SUV sales webpage several times, looked at the car and downloaded a financing application. 

b) As they get older, ladies prefer an SUV over a convertible.


At the next step, these kind of data get bundled up (centralised), with signatures being derived from the entire data pool. 

Step 3

Customer signatures

A signature is a record of relations and connections in the data pool. Depending on the criteria related to the targets, patterns in purchasing behaviour may be identified.


In our case, combining the insights of the sales person with the insights of the marketing department, the company may conclude that Mr. Moore and Ms. Smith should be offered an SUV - just like other potential customers with a similar profile. The two sample customers are therefore part of a common customer segment ("cluster"), even though they may not have any apparent similarities.


Thanks to the Refining Smart Data process, the car manufacturer may discover the invisible similarities and connections and target them appropriately. Practically, this might be via an invite to an event (= raising awareness), or making a strategic offer as part of a recommendation (advocacy). 

Step 4

Statistics

Once all available data have been statistically evaluated according to signatures, our car manufacturer may realise that 30% of their potential customers have a profile similar to Mr. Moore and Ms. Smith. Therefore, they may wish to launch an advertising campaign in order to boost the SUV sales in this particular customer segment. 


The statistical data are simple in principle, but they may not be used for the purpose of creating models or controlling campaigns. However, a statistical overview may indicate the order of magnitude and distribution. 

Step 5

Modeling

The larger the amount of data available, the more difficult it is to grasp them and make appropriate decisions. Our custom models provide automated calculation, which isolates Ms. Smith and Mr. Moore from the common cluster. This new segmentation is created with a statistics app. 

 

Our car manufacturer thus gets to know its customers better and may target their SUV campaign to potentially interested parties from the Moore-Smith cluster. 

Step 6

Implementation

With the new segmentation, tens of thousands of A/B tests rather than a single one are being carried out in the campaign, using a variety of criteria. The Refining Smart Data process allows to divide the data into smaller blocks - the so-called clusters, as well as to continuously refine and automatically evaluate them using artificial intelligence algorithms. 

 

For their pilot project, the car manufacturer may decide to launch a campaign on Facebook, which in the course of the project will be monitored and optimised. 

Step 7

Visualisation

The AI-based advertising campaign provides insights that go much further than merely identifying whether an interested party clicked on Option A or Option B. Our car manufacturer may now not only determine the specific video ads that are performant for a specific customer profile, but also see the exact criteria of the customers highly likely to buy an SUV. 


The visualisation dashboards do not provide only the "conversion rates" or the “target group sales during the campaign”, the reason being that the campaign modeling revealed the consideration of the "sales of the control groups" as well as the "over-performance of the participants compared to standard segments" to also strongly impact on the success of the campaign. The company's marketing department may now easily compare individual activities (e.g. of the test group, the control group or the holdout group). 

Step 8

Monitoring

For the modeling, the minimum requirement is to calculate ranks within the potential target group - or, as in the case of our car manufacturer, to calculate the conversion probability within the newly defined customer segment. 


As part of the Facebook campaign, a target list with 100,000 customers has been created and divided into target groups according to scores. 


During the campaign, it may be established that the interested parties who - like Ms. Smith - are particularly interested in insurance benefits, go on to achieve a significantly higher score. 

Step 9

Check

Now is the moment to analyse and evaluate the model, i.e. the advertising campaign in our case. The scoring of the different target groups gets adjusted and refined again, according to the campaign results. 


What new insights may our car manufacturer gain from the campaign? 


For people like Ms. Smith, the interest in an SUV arises from an increased need for safety. People want to be safe on the roads as they grow older. With this knowledge, the automobile manufacturer is planning another campaign in cooperation with an insurance company. 


The Refining Smart Data process enables the company to automatically automatically generate and run campaigns faster and more precisely, as well as better tailored to the customer. The data-based modeling and detailed control of the campaigns give the company precise, measurable results”. The car manufacturer now has a profound insight into its own customer base and market dynamics. With the consistent continuation of the Refining Smart Data process, the company can Generate "knowledge from data" on an ongoing basis and thus secure competitive advantages. 

Use Case: Insurance

Step 1

Business Case

Mr. Harrison, the sales manager of the insurance company Savesta wishes to reduce risks in contracts with new customers by means of a data-driven risk assessment. This way, Mr. Harrison's sales team may be able to make estimates accessing real-time insurance policies assessment. 

Step 2

Raw data

The insurance company has 4 million poste code records from the UK and Ireland with a granular risk potential for frost, wind damage, flood or burglary. 

Step 3

Customer signatures

At the next step, signatures may be generated related to the building type, age, value or cost of repairs. 


Until the present moment, the risk analysis at Savesta had been based on the experience of individual insurance brokers and their data management ability. Although Mr. Harrison attaches great importance to continuous training of his sales team, problems with new customers have been arising time and again, with the damage risk having been estimated as too low by the customer advisor, resulting in higher costs for the insurance company. 

Step 4

Statistics

Using a matrix combining the signatures with the postcode areas, policies can now be calculated according to the regional risks. 


With these fully automated calculations, Mr. Harrison may be able to minimise the error rate in the risk analyses conducted by his sales team. 

Step 5

Modeling

The modeling is carried out by the insurance company's actuary as standard. Supported by the data-driven risk analysis, the actuary can adapt the calculation of the policies to match the company’s desired results as well as optimise them sustainably. 

Step 6

Implementation

From the refined data, based on the data such as location and building type, a web-based premium-calculating app may now be developed. In the event of an increased risk potential, policies can automatically become rejected or subject to further individual approval. 

Step 7

Visualisation

The weekly reporting gives Mr. Harrison an overview of the number of app users in specific postcode areas, as well as the ability to compare an application filed by a customer against the policy being applied for. 

Step 8

Monitoring

Savesta’s in-house actuary is responsible for the monitoring of the campaign, as well as defining models. 

Step 9

Check

Project results get checked and, if necessary, further adjusted according to the risk database, in order to promote an optimisation of the premium calculation. Adjustments with the new data from the risk database may take place annually. 

Use Case: Sales & Distribution

Step 1

Business Case

A company that sells hard drives to IT wholesalers wishes to optimise its business structure, achieving more balance between the requirements of the internal and external parties. 


The following requirements are to be taken into account:

a) The Product management team are aiming for the highest possible prices.

b) The Sales team are aiming for the lowest possible prices.

c) The Wholesalers are aiming for the lowest possible inventory levels in wholesale.

d) The Manufacturer is aiming at the highest possible stock levels in wholesale, in order to increase their competitiveness. 

Step 2

Raw data

Stock levels, sales (per week), forecasts and target prices are used as the basis for data analysis. In addition, prices from comparable products by competitors are being integrated into the database. 

Step 3

Customer signatures

After the raw data has been centralised (i.e. all relevant date linked and bundled up), an overview of the individual products and product groups may be created. These product signatures indicate trends across various corporate divisions, showing the way to specific solutions for sales optimisation.

Step 4

Statistics

Using model calculations, the company may now make assumptions about the impact of their price adjustments - relative to the market shares and margins. 

Step 5

Modeling

The model calculation reveals the company’s greatest potential harboured in the sale of product bundles. By sponsoring these bundles, high-demand products may be combined with high-stock products. 

Step 6

Implementation

The company’s sales partners may use bonuses to make the sale of the product bundles more attractive. By means of a targeted adjustment of the inventory, the company may become more profitable as well as meet the requirements of every party involved in the sales process. 

Step 7

Visualisation

Dashboards with a traffic light system display the model as well as its results, allowing an easy and simple analysis. These dashboards connect the sales to the price level (relative to the minimum price), the stocks to the manufacturer’s central warehouse as well as enable the product groups assessment.

Step 8

Monitoring

Monitoring based on a traffic light system is crucial in balancing of all relevant business areas. Taking individual parameters of product groups to account, it allows to accurately determine a specific parameter becoming leading, e.g. If a parameter enters the red traffic light zone. Every other parameter, e.g. price increase or decrease, an increase or decrease in the stocks, is subsequently monitored and managed according to these criteria.

Step 9

Check

In order to identify any acute or potential bottlenecks in advance, the modeling parameters may be adjusted quarterly. 

Use Case: Telemarketing

Step 1

Business Case

A telemarketing agency wishes to carry out a budget-optimised cross-selling campaign for a company with an established base of 250,000 potential customers for the advertised product.


The Refining Smart Data process may help define the customer accounts particularly suitable for the cross-selling action. The project budget allows for a maximum of 1,000 calls, with the minimum project turnover amounting to 50,000 Eu. The goal is to qualify 20 BANT leads with the high purchase probability, whereby; 


B = budget (determined budget) 

A = Authority (the client is the decision-maker)

N = Need (the need exists and can be quantified) 

T = Time (determined project period) 

Step 2

Raw data

First, all relevant available data are being identified, e.g. the demographic data (i.e. the industry, location, employees), the sales figures for the last six years as well as the relevant contact details. In addition, current leads and the most recent sales and telemarketing contacts are being excluded. 


The data gets cleaned up so that any incorrectly entered or recorded data do not get included in any of the subsequent calculations, thus eliminating the potential errors and miscalculations.

Step 3

Customer signatures

Based on the raw data available, trends and market shares may now be determined in combination with internal segmentations (Class A, B, C). 

Step 4

Statistics

Using statistical methods and calculations, potentially successful models may be identified. 

Step 5

Modeling

A response model may be created for the telemarketing agency based on the database signatures. From the initial 250,000 potential interested parties’ data, approximately 150,000 addresses remain.


For each of the data on record, a score may now be calculated, allowing the client Ito focus their sales activities on high purchase probability clients.

Step 6

Implementation

The telemarketing agency performs the calls according to the latest response model available, reporting on the results and feeding them back into the database. 

Step 7

Visualisation

The results of the pilot project are now getting visualised in order to get a comprehensive overview of the key figures. The visualisation makes it easy for those key figures to be compared and evaluated.

Step 8

Monitoring

Modeling can be further improved by control groups assessment. This is how a simple response model can become a predictive incremental response model.


This extended model helps predict the likelihood of the success of future actions. For example, it is possible to precisely determine the moment when a cross-selling campaign has passed its peak. 

Step 9

Check

Once the campaign finishes, it may be examined and refined using the Refining Smart Data process, suggesting alternatives where necessary, based on the current campaign outcomes.

Reiner Rübel

Reiner Rübel

The secret to successful campaigns…

With Refining Smart Data, we can make your day-to-day business and your strategic development much easier. 


The refined workflow of your campaigns allows you to quickly identify high-quality leads, get supported along the customer journey as well as actively monitor and manage the customer experience. 

Georg Klauser

Managing Director 
Boston

Georg Klauser

Managing Director 
Boston

In order to establish our company on the German-speaking market, we commissioned Corvendor to develop an appropriate business plan. Corvendor have supported us very efficiently in setting up our reporting system, sales management, segmentation and online sales platform.


Our decision to invest in the DACH market was based on the expertise that our parent company in the UK received in reporting, KPI calculation and data modelling.
Their extensive expertise in retail and sales management allowed Corvendor to design a business plan with projected coverage and turnover; we reached these targets within our 2-year start-up phase. Especially in the first 6 months, we have had a very close collaboration with Corvendor. As of the second quarter, we were able to kickstart our sales thanks to their detailed planning.


Their proven expertise in online marketing and data management as well as their excellent implementation of the business plan have made our collaboration with Corvendor extremely satisfactory.

Our experts

Our core team consists of Reiner Rübel and Prof. Dr. Stefan Rüger, with affiliated experts available whenever and wherever needed. 

Reiner Rübel

Reiner Rübel
Marketing Consultant

Founder & managing director of Corvendor GmbH


Core competencies: Data-based marketing campaigns for lead generation and customer acquisition for enterprise and medium-sized customers. 

Prof. Dr. Stefan Rüger

Prof. Dr. Stefan Rüger

Data Mining

Professor of Computer Science at the Open University Milton Keynes


Core Competencies: Doctorate in Computer Science in the field of neural networks. Research in "Multimedia Information Retrieval". Professor for "Knowledge Media" at the Open University, Milton Keynes, advisor on the topics of "Data Mining" and information management projects. 

Dirk Niemann

Managing Director ERGO Insurance UK Branch

Dirk Niemann

Managing Director ERGO Insurance UK Branch

As a large insurance company, we receive thousands of settlement claims every day, which need to be processed in great detail by our consulting partners. We have experienced too too many claims being written off, falling outside the scope of their local risk group. Therefore we urgently required a management tool that works with accurate data from local risk assessments for storm, hail, flood, terrorism, theft, etc.


Corvendor have set up an online management platform which allows our consulting partners in the UK and Ireland to reliably calculate insurance premiums based on the actual risks in the 2 million postcode areas concerned. This accurate risk assessment that Corvendor’s online management platform allows, has led to a significant increase in our margin and was perfectly tailored to our needs.


We were also impressed by the developers' fast work: after only 6 weeks we were able to thoroughly test the prototype of the web application. The first test version, which was sent to selected business partners, was ready-to-use after about 6 months only. In the meantime, our database and online platform have had their performance and data sets updated annually.


We are delighted to recommend Corvendor as we have been fully convinced of both the speed and the high quality of their work!

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