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How attribution data has informed the affiliate channel

I recently met Helen Southgate, Online Marketing Controller for Strategy & Planning at Sky, presenting their approach to media channel attribution and how it informed their understanding of the affiliate channel in context with other online activity. With that in mind she kindly agreed to this interview where I asked about how BSkyB manage their affiliate marketing. You may also find her presentation interesting for reference:

1. You have been talking attribution for some time at Sky. How did you begin the process?

Yes, I have been talking about it for some time and to be honest it’s still in progress. It’s a massive project and one that is forever changing given the dynamic nature of online. I began the process by looking at what data we had access to and where it was being captured. Since then I’ve focused in on the paid marketing side of attribution, what I’d term “natural” attribution will be a phase 2, so we still have some way to go.

2. How did you decide upon which technology to use?

If I’m going to be completely honest, this was inherited but I don’t think the platform is the challenge. The challenge is firstly ensuring all of your activity is tracking and you are comparing apples with apples. The next challenge is actually what you do with all of the data.

3. Once you have the data how do you begin to interpret it?

Good question, this I think is the trickiest part of attribution. Most people think it’s getting the data but in my opinion it’s what you do with it.

At the moment we’re looking at data on a campaign basis, so say over 4-6 weeks. The key thing is to start basic then drill down into more granular detail.

4. What for you are the biggest arguments in favour of attribution?

The output of this could be more effective use of marketing spend, identification of new opportunities or improvements to the customer journey, all massive wins for any marketer.

5. What have you found to be the greatest challenges?

Time and Resource – it takes a lot of people to get this right and it takes a lot of time to interpret the data into something that is both insightful and useful.

6. Can you give examples of some of the practical changes to your marketing activity you have made as a result of your work on attribution? 7. Working with the amount of data you have at Sky and the number of stakeholders, how did you cope with different departments’ agendas? How much of a challenge was it to look at the data impartially?

My role sits outside of any specific media channel so I’m in a unique position to be able to look at this agnostically. This is really important; as it is very easy to have a bias, when I worked across paid search and affiliates I would undoubtedly have had a bias to those channels, it’s human nature!

It is also really important to go into this with no preconceptions about what the data will tell you or what question you want it to answer. You can make data do anything so by going into a project without an open mind and no agenda you will bias the outcome.

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Customer Data Value For Marketing

A comprehensive checklist for auditing different customer data types in a CRM or Email marketing system

In today’s world of ever-increasing data availability, volume and variety the challenge to know which data is valuable to you is a key step in starting to build a marketing solution. An often-cited response is that ‘all data is important’ and this may be true, but to help decide which elements are critical in the initial stages of building your solution a method to identify at the value of each type of customer data is key.

In this post, I will look at how to audit customer data based on its type and value.  The examples will show why it’s important to be selective when reviewing customer data in CRM and Email marketing.

Over numerous implementations of Marketing Database solutions, I have seen many types of data, including ‘pet’s name’, ‘favourite colour’, ‘number of car doors’ which all have potential value to different markets:

Pet’s Name – Pet Supplies Retailer.

Favourite Colour – Retail, particularly clothing.

Number of Car Doors – Motor Insurance industry.

When first considering each data element, the ability to classify it can help determine how valuable and which phase of a solution it should be delivered in, if at all.

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The following list provides examples of data elements and will help you quickly identify the critical pieces of information to your business and goals from the various different data sources. Typically the priority order of the data is as follows:

1. Customer Identity Data

At the heart of database marketing is the individual, so knowing who the individual is and being able to build and maintain a Single Customer View provides the first type of data, Identity. This includes any information which enables an individual to be uniquely identified and includes:

Name Information – Title, First Name (Forename), Last Name (Surname), Designatory letters, etc.

Person Information – Date of Birth, Gender, etc.

Postal Address Information – Building Number, Building Name, Address Lines, Town, County, Postal/Zip Code, Country, etc.

Telephone Information – Home Telephone No., Work Telephone No., Mobile No., etc.

Email Address Information – Personal Email Address, Work Email Address, etc.

Social Network Information – Facebook Identifier, Twitter Address, Linkedin identifier, etc.

Account Information – Details of your customer’s account ids or user ids.

Job Information – Company Name, Department Name, Job Title, etc.

Permission and Suppression Data – Not distinctly an identity element of data, but equally important is the information concerning permission to communicate and reason for not communicating (suppressions).

2. Quantitative Data

Once you understand who the individual is the next key element is the measurable operational data, which enables you to understand how your customer has behaved, transacted or reacted with your business. This includes any information which describes activity completed between the customer and your business:

Transactional Information (Online and Offline) – Number of products purchased, actual products purchased, Order/Subscription Value, Order/Renewal dates, product abandonments (abandoned baskets), Product Returns, etc.

Online Activity – Website visits, product views, online registrations, etc.

Social Network Activity – Facebook likes, Twitter interactions, etc.

Customer Services Information – Complaint details, customer query details, etc

3. Descriptive Data

Understanding who the individual is and the type of activities they complete with you provides a good starting point for any marketing database. To gain a fuller perspective of your customer additional profile information is crucial. This provides additional information about your customer, beyond the identity and quantitative details, covering:

Family Details – Marital status, number of children, age of children, etc.

Lifestyle Details – Property type, car type, number of car doors, pet ownership, etc.

Career Details – Profession, Education level, etc.

4. Qualitative Customer Data

The final type of data you will come across provides further description of your customer and potential behaviour and is usually provided by questionnaire type information where an attitude, motivation and opinion is provided:

Attitudinal information – How do you rate our customer service, how do you rate the value of the product, how likely are you to purchase our product again, etc?

Opinion – What is your favourite colour, where is your favourite holiday destination, etc.

Motivational – Why was the product purchased (personal use, gift for someone, etc), what was the key reason for purchasing our product (locality, price, quality), etc.

Using this simple classification process and relating them to your core business goals, will enable a quick identification of which data provides the information critical to the core success of your business. This can then be used to plan the appropriate delivery phases, with clear understanding of the value achieved from each data item included, enabling you to answer the question ‘How valuable is knowing my customer’s pet name?’ to your business.

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How Big Data Can Revolutionize Agriculture

Big data is becoming pervasive by introducing more sophisticated ways to exploit roots of technology. Not only user interfaces but also necessary tools have evolved drastically. Big data has made the world truly close, and yes, the customised data choice is a cherry on the cake. Big data tools and its results have entered into almost every segment of human lives. Just say the name and big data is there. Actually, data is everywhere, it needs to be handled professionally to take out gold from ash. The agricultural segment is the backbone of the Indian economy. Not only India, the existence of humanity is having a knot with the yield from the land. The world is changing, things are changing, the climate is changing, and humans have already adopted those changes. But the motherland hasn’t. According to a survey, the world population will be having a boom real soon by hitting around 47% growth by 2040. Now that’s a warning bell for human existence. The overexploitation of natural resources and lack of strategic decisions has led all of us to a situation where the balance of nature has shifted to a whole new different level. To tackle the future food crisis, technology has to be used to analyze and modify the existing agricultural practices. Here, big data comes into the picture. Let’s have a quick overview of the ways in which big data can be deployed to evolve agricultural segment.  

1) Generation of Data Sets by Revealing Food Systems

Data has enormous power to turn things upside down, but only when it is used effectively. The data can only be used wisely if it is converted into segregated data sets. The agricultural segment has a long list of attributes that can be taken into consideration for the proposed analysis and consequent result studies. Key attributes having the impact on the process output can be handpicked and used for generating data sets. These data sets will be used to produce a ground for all related activities. Every food systems have different structure and these can be easily analysed only and only if, the data set implementation is done.  

2) Monitoring of the Trend

All the data relating to the history of specific crop disease or pest can be used to generate the data set and consequently monitoring of this data may lead to unfolding the trend in the agricultural field. Nowadays, predicting exacts things are nearly impossible. All the attributes have become so arbitrary that nothing can be guaranteed. But monitoring these attributes, for instance, the pest and crop disease history, data monitoring can be used to predict the future attacks on yield so that preparatory actions could be taken. This will not only save the stakeholders money but also the time investment. Thus, monitoring the selected attributes has an enormous importance in the implementation of big data.  

3) Impact Assessment

Every system is designed with the consideration of risk analysis. Every wrong turn has to be considered before it is taken. The probable impacts and corrective actions for the same have to be defined. Same goes with the agricultural segment. Today, there is a number of unfortunate situations where the whole yield in the field is wasted due to some uncertainties. These things can be managed well if the impact assessment is done properly. For instance, if the impact assessment for pesticides is done at the very first stage of sowing the seed then the probable failure can be prevented. In any unfortunate situations, if the pesticide turns out to be dangerous, then the impact analysis helps to avoid the consequences. Necessary measures can be taken to avoid the wrong turns and help in taking corrective actions.  

4) Data-Driven Farming

As per the current scenario, decision makers are facing tremendous problems in predicting probable failure. Here, data is the saviour. Data can be used effectively to conclude predictions thus preventing them in taking risky decisions. Today, data sources including satellites, mobile phones, weather stations have contributed in making this possible. For an error proof analysis, the data quality and variance is a must thing. And the data source serves for both of the necessities. What to plant? When to plant? These basic questions can be answered very easily if the data backs it up. The dream of data-driven farming is slowly making its move and proving it with improved yields.  

Summary

Big data is becoming pervasive by introducing more sophisticated ways to exploit roots of technology. Not only user interfaces but also necessary tools have evolved drastically. Big data has made the world truly close, and yes, the customised data choice is a cherry on the cake. Big data tools and its results have entered into almost every segment of human lives. Just say the name and big data is there. Actually, data is everywhere, it needs to be handled professionally to take out gold from ash. The agricultural segment is the backbone of the Indian economy. Not only India, the existence of humanity is having a knot with the yield from the land. The world is changing, things are changing, the climate is changing, and humans have already adopted those changes. But the motherland hasn’t. According to a survey, the world population will be having a boom real soon by hitting around 47% growth by 2040. Now that’s a warning bell for human existence. The overexploitation of natural resources and lack of strategic decisions has led all of us to a situation where the balance of nature has shifted to a whole new different level. To tackle the future food crisis, technology has to be used to analyze and modify the existing agricultural practices. Here, big data comes into the picture. Let’s have a quick overview of the ways in which big data can be deployed to evolve agricultural chúng tôi has enormous power to turn things upside down, but only when it is used effectively. The data can only be used wisely if it is converted into segregated data sets. The agricultural segment has a long list of attributes that can be taken into consideration for the proposed analysis and consequent result studies. Key attributes having the impact on the process output can be handpicked and used for generating data sets. These data sets will be used to produce a ground for all related activities. Every food systems have different structure and these can be easily analysed only and only if, the data set implementation is chúng tôi the data relating to the history of specific crop disease or pest can be used to generate the data set and consequently monitoring of this data may lead to unfolding the trend in the agricultural field. Nowadays, predicting exacts things are nearly impossible. All the attributes have become so arbitrary that nothing can be guaranteed. But monitoring these attributes, for instance, the pest and crop disease history, data monitoring can be used to predict the future attacks on yield so that preparatory actions could be taken. This will not only save the stakeholders money but also the time investment. Thus, monitoring the selected attributes has an enormous importance in the implementation of big data.Every system is designed with the consideration of risk analysis. Every wrong turn has to be considered before it is taken. The probable impacts and corrective actions for the same have to be defined. Same goes with the agricultural segment. Today, there is a number of unfortunate situations where the whole yield in the field is wasted due to some uncertainties. These things can be managed well if the impact assessment is done properly. For instance, if the impact assessment for pesticides is done at the very first stage of sowing the seed then the probable failure can be prevented. In any unfortunate situations, if the pesticide turns out to be dangerous, then the impact analysis helps to avoid the consequences. Necessary measures can be taken to avoid the wrong turns and help in taking corrective chúng tôi per the current scenario, decision makers are facing tremendous problems in predicting probable failure. Here, data is the saviour. Data can be used effectively to conclude predictions thus preventing them in taking risky decisions. Today, data sources including satellites, mobile phones, weather stations have contributed in making this possible. For an error proof analysis, the data quality and variance is a must thing. And the data source serves for both of the necessities. What to plant? When to plant? These basic questions can be answered very easily if the data backs it up. The dream of data-driven farming is slowly making its move and proving it with improved chúng tôi data has evolved the way things work. Now, it’s a turn for the agricultural segment. Many researchers are toiling their nights to make it more and more accessible, dependable and of course yieldable. Today, agriculture segment need to evolve to preserve human existence on the earth, and undoubtedly big data can help do this. The above-noted steps can be genetically followed to develop and implement procedures to yield good results. Hopefully, the near future will evidence the utopia in agriculture backed up with green evolution.

Have 80% Of Apple Watch Owners Used Apple Pay?

Have 80% of Apple Watch owners used Apple Pay?

Today a study has been released in which Apple Pay appears to be being used by 80% of all Apple Watch users. Carried out by Wristly, a private research group, it’s a headline grabbing figure for Apple’s mobile payments service, which gets a dedicated button on the Apple wearable. Nonetheless, as with any such study, there are some lingering questions to take into account.

Wristly reached out to a collection of people they call their “Inner Circle,” people who have signed up with the company as Apple Watch owners. You need to have an Apple Watch for as long as you’re a member of the Inner Circle.

Wristly isn’t a traditional analyst group, per se. Instead, they’re focused on the Apple Watch as a “catalyst” for wearable growth in general. Below you’ll see the company’s “About” page.

The Wristly Inner Circle asks the following of you: “Once a week, [Wristly] will ask you 5 quick questions, and in return you will be first to get the insights.”

The first question asked was “How did you first use Apple Pay?” Here’s where the 80% comes from – every respondent who has used Apple Pay at least once, even if they’ve only used Apple Pay once.

The majority of these respondents suggest that they’ve used the iPhone to pay for something with Apple Pay – 56% of the total used an iPhone in a retail environment with Apple Pay. 19% actually used the Apple Watch to use Apple Pay for the first time.

That works out to approximately 190 Apple Watch users in this study – out of a total of 1013 Apple Watch owners – who say they were introduced to Apple Pay via the wearable.

In the research paper “Wristly Insights”, it’s claimed that “various surveys published in 2024” suggest that “Apple Pay usage level” on the iPhone 6 was at around 15% to 20%.

Later in the study they take a portion of the set of those that’ve used Apple Pay in the past and ask them the following:

The study goes on to say that “All in all, our research suggests Apple Pay on the Watch is a delightful experience…” followed by a survey question which asks which way these Apple Watch users prefer to use Apple Pay.

Finally come “Statements about the Apple Watch and Apple Pay” ranking system. Users were asked to read statements about the Apple Watch and say whether they Agree, Disagree, or Neither Disagree or Agree.

While studies of this sort are interesting, the relatively small subset of respondents does require taking into account before too many conclusions are drawn. Until Apple itself gives us some solid figures, it’s hard to know exactly how widespread Apple Pay is among Apple Watch wearers. That may well happen on September 9, when the Cupertino firm is expected to hold an event to launch the new iPhone 6s, among other things.

[Article updated 8/18 to clarify first-usage statistics]

VIA TechCrunch

SOURCE Wristly

Can Big Data Solutions Be Affordable?

One of the biggest myths still remains that only big companies can afford Big Data driven solutions, it is appropriate for massive data volumes only and is expensive as a fortune. That is no longer true, and there were several revolutions that changed this state of mind.  

Maturity of Big Data technologies

The first revolution is related to maturity and quality. There is no secret that ten years ago big data technologies required certain efforts to make it work or make all pieces work together.

Picture 1. Typical stages, growing technologies pass-through   There were countless stories in the past coming from developers who wasted 80% of time trying to overcome silly glitches with Spark, Hadoop, Kafka or others. Nowadays these technologies became sufficiently reliable, they eliminated childhood diseases and learned how to work with each other. There is a much bigger chance to see infrastructure outages than catch internal bugs. Even infrastructure issues can be tolerated in most cases gently as most big data processing frameworks are designed to be fault-tolerant. In addition, those technologies provide stable, powerful and simple abstractions over calculations and allow developers to be focused on the business side of development.  

Variety of big data technologies

  Picture 2. Big Data technology stack Let’s address a typical analytical data platform (ADP). It consists of four major tiers:

Dashboards and Visualization – facade of ADP that exposes analytical summaries to end users.

Data Processing – data pipelines to validate, enrich and convert data from one form to another.

Data Warehouse – a place to keep well-organized data – rollups, data marts etc.

Data Lake, place where pure raw data settles down, a base for Data Warehouse.

Every tier has sufficient alternatives for any taste and requirement. Half of those technologies appeared within the last 5 years.

Picture 3. Typical low-cost small ADP  

Picture 4. ADP on a bigger scale with stronger guarantees  

Cost effectiveness

The third revolution is made by clouds. Cloud services became real game changers. They address Big Data as a ready-to-use platform (Big Data as a Service) allowing developers to focus on feature development, letting cloud care about infrastructure. Picture 5 shows another example of ADP which leverages the power of serverless technologies from storage, processing till presentation tier. It has the same design ideas while technologies are replaced by AWS managed services.

Picture 5. Typical low-cost serverless ADP Worth saying that the AWS here is just an example, the same ADP could be built on top of any other cloud provider. Developers have an option to choose particular technologies and a degree of serverless. More serverless it is, more composable it could be, however more vendor-locked it becomes as a down side. Solutions being locked on a particular cloud provider and serverless stack can have a quick time to market runway. Wise choice between serverless technologies can make the solution cost effective. Usually, engineers distinguish the following costs:

Development costs

Maintenance costs

Cost of change

Let’s address them one by one.  

Development costs

Cloud technologies definitely simplify engineering efforts. There are several zones where it has a positive impact. The first one is architecture and design decisions. Serverless stack provides a rich set of patterns and reusable components which gives a solid and consistent foundation for solution’s architecture. There is only one concern that might slow down the design stage — big data technologies are distributed by nature so related solutions must be designed with thought about possible failures and outages to be able to ensure data availability and consistency. As a bonus, solutions require less efforts to be scaled out. The second one is integration and end-to-end testing. Serverless stack allows creating isolated sandboxes, play, test, fix issues, therefore reducing development loopback and time.  

Maintenance costs

 One of the major goals that cloud providers claim to solve was less effort to monitor and keep production environments alive. They tried to build some kind of ideal abstraction with almost zero devops involvement. The reality is a bit different though. With respect to that idea, usually maintenance still requires some efforts. The table below highlights the most prominent kinds.

Cost of change

Another important side of big data technologies that concerns customers — cost of change. Our experience shows there is no difference between Big Data and any other technologies. If the solution is not over-engineered then the cost of change is completely comparable to a non-big-data stack. There is one benefit though that comes with Big Data. It is natural for Big Data solutions to be designed as decoupled. Properly designed solutions do not look like monolith, allowing to apply local changes within short terms where it is needed and with less risk to affect production.  

Summary

As a summary, we do think Big Data can be affordable. It proposes new design patterns and approaches to developers, who can leverage it to assemble any analytical data platform respecting strongest business requirements and be cost-effective at the same time. Big Data driven solutions might be a great foundation for fast-growing startups who would like to be flexible, apply quick changes and have short TTM runway. Once businesses demand bigger data volumes, Big Data driven solutions might scale alongside with business. Big Data technologies allow implementing near-real-time analytics on small or big scale while classic solutions struggle with performance. Cloud providers have elevated Big Data on the next level providing reliable, scalable and ready-to-use capabilities. It’s never been easier to develop cost-effective ADPs with quick delivery. Elevate your business with Big Data.  

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4 Tips To Turn Big Data Into Marketing Revenue

If you’d like to know some tips on how you can best utilize big data to boost your earnings, here are some suggestions you can consider

Companies use big data to analyze their customer base and explore opportunities in their market space. Big data refers to technologies that store, analyze, and manage large and complex data. Through customer data, the needs of the consumers can be easily identified and catered to by providing improved goods or services. It also eliminates the guesswork for marketing strategists as they can quickly determine a customer’s purchasing behavior and use it as a basis for campaigns.  

Ultimately, the goal of collecting and analyzing customer data is to know their profile, study their interests and product preferences, and guide them towards a successful sale or transaction with the company. In essence, big data is turned into marketing revenue through various methods.  

If you’d like to know some tips on how you can best utilize big data to boost your earnings, here are some suggestions you can consider:  

Employ Big Data To Enhance Marketing Strategies

Customer data is an excellent tool to help you design effective marketing campaigns. By analyzing customer profiles, you can better understand your target audience. In effect, this understanding will help you curate a campaign that would attract attention, pique the market’s curiosity, and invite new and repeat business.  

For example, you can utilize cookies gathered from the customer’s web activity to learn their interests, purchase histories, and general profile. With this information, you can tailor your subsequent campaigns in a manner that would best suit your target audience. This way, you can minimize strategic errors that may hinder the success of your campaigns. In addition, you can also help your enterprise save time, effort, and resources in your marketing strategies moving forward.  

Create Data-Based Customer Engagement Strategies  

You can also use big data to design strategies that augment customer engagement. For instance, you can study how your target audience interacts with your brand and the factors that boost their engagement. You can also identify

how to increase customer value

through these interactions, online or otherwise.    

Big data analysis can also provide you with crucial information to make adjustments where needed. For instance, you can observe which of your existing products gets the most and least engagement. This way, you can devise a plan to help attract more attention towards less-engaging items or realign your resources towards developing new and improved products.  

Boost Brand Awareness And Customer Acquisition Through Big Data  

As you collect digital information based on your customer’s responses on your online platforms, you can also determine how to widen your brand’s reach and boost awareness in other market platforms. One way you can do this is to increase your brand’s presence on the sites that your target audience frequents.   

For instance, you can design an online campaign via social networking sites popular across your customer base. This way, it will be easier for them to engage with your brand and share your product information with their network. Also, while you promote brand awareness, you can improve customer acquisition by engaging with audiences connected to your current customer base.  

Use Big Data As Basis For Adjusting Price Points 

Product prices can significantly influence a customer’s purchasing behavior. As such, it’s crucial to be aware of price movements in your market and how your brand stands against the competition. While offering lower-priced products may seem like the best way to beat competitors, it can backfire if the price adjustments are not justified. For instance, the customers may question quality or brand credibility if the prices are too low compared to other brands. 

With these factors in mind, you’ll need to make price point comparisons using customer data that shows how product selections are influenced. You may see specific purchasing patterns that may help you point out the audience’s primary considerations in choosing a brand or an item. This way, you can make reasonable price adjustments that won’t hurt your brand and your revenue.  

Meanwhile, you can also explore other options to make your prices more competitive. For example, you can consider adding discounts and freebies, which can help your products stand out from other brands.  

  

Conclusion 

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