Analytics Paving the Road to Sales and Partner Alignment

Working through the IT channel can provide companies with huge opportunities to scale their business quickly, particularly if they need to build a broad geographic or industry reach. However, if mismanaged, the relationship can be frustrating for all the partners involved.

Why is this? Why do some companies run extremely effective channels, while others seem to miss the mark?

Answering this means taking a hard look at the personalities and priorities involved, and more importantly what data is available to partners — as well as the vendor. It’s difficult to execute an effective channel sales strategy without knowing which types of partners perform well in certain markets. While many companies on the vendor side will recognize the phrase “just consider us an extension of your sales force”, the truth is that ultimately all partners will make the decisions that are in their own best interests.

It is up to the vendor to invest in its channel partners, the ones that work best for them. In most cases, quality is better than quantity. Too often companies focus primarily on partner acquisition rather than partner retention and success, figuring that every new partner should be a steady source of future revenue. In fact, a minority of channel partners usually account for the vast majority of vendor deals. However, working out which partnerships will succeed is difficult.

Using analytics can help both vendors and their channel organizations. Many vendors only rely on their experience of what is happening in the market to build their channels. Using data to supplement this experience creates a more powerful direction. Segmentation of channel models, planning for service provider support, versus a more traditional reseller, as well as adding elements of personalization and designing incentive plans based on partner value has to go beyond instinct.

Analytics can also help partners gauge and measure their own success. Should they specialize on one particular market, or expand their vendor list to reach a broader range of prospects? What will training, accreditation and marketing for each vendor cost, compared to the potential for new business? Answering these questions without data means many channel organizations are relying on hunches and smaller wins to guide them towards the future.

Regional and industry benchmarks and peer group comparisons can show where companies are lagging behind, and where they have opportunities for growth.

While some resellers and consulting companies only look at analytics as a hot product to go to market with, the opportunity is not just about selling related products but in finding way to improve operations, marketing and sales strategies. By factoring in market trends and public sources of data such as seasonal changes, competitor pricing and category popularity changes, the most profitable markets for products or services they offer can be more easily identified.

Analytics offerings can also allow them to run price sensitivity analysis, and using data as a way to ask vendors for partner pricing schemes that maximize profit margins for both the vendors and the partners. Analytics can even facilitate discussions around collaborating on programs and campaigns that create a joint competitive advantage and market differentiation for both parties.

The challenge here for everyone involved in the channel is to make sure that analytics is something that we all know, understand and use. For companies that make the best use of analytics, there are far greater opportunities than those that don’t.

This article originally appeared on CRN UK.

The Modern CMO: Part Art, Part Science and Part Religion

It is the art of marketing that has made “Got Milk?” and “Just Do It” among some of the most recognizable campaigns of our time. Why? Because we related to them in the way we live our everyday lives. But as much as art and creativity remains an important factor for CMOs – and great brands will always remain fresh in our minds – the line between marketing art and marketing science are quickly blurring. Being the Don Draper of marketing is no longer the most revered marketing talent. Top leaders in marketing are good at both the art and the science of marketing. They deploy powerful technologies, like analytics, to build long-term strategies, deliver value to their business and ultimately guide them toward marketing success.

We all took note when the Harvard Business Review declared that being a “data scientist” is the sexiest job of the century. But what’s the correlation between data scientists and marketers?

We know that data scientists are the data ambassadors through whom we understand information. They are subject matter experts who direct and provide mentoring to others in the organization who want to interact with data, right? Not true!

It turns out that data science and analytics has made its way into marketing. In a recent study carried by OMI, Fortune 500 companies and global agencies identified analytics as the largest talent gap in digital marketing.

In some organizations, the need for analytics has gotten so critical that they are hiring a Chief Marketing Technologist. Last year, Gartner reported that 72 percent of high-tech marketing teams indicated there was a “chief marketing technologist” type role in their organization. These individuals, among other things, use behavior-based data to identify best sales opportunities and improve brand awareness.

So there is the art and science of marketing, but where does that leave us with religion?

Creating an important role for analytics is one thing, but practicing an analytic agenda is another. Only the most analytically-religious marketers extract real value from big data and analytics.

In the latest edition of the CMO Survey, more than 400 CMOs said they expect to increase their spending on marketing analytics by 72 percent over the next three years, but even though marketers are allocating more money to analytics, only a few are using analytical insights to measure marketing ROI.

Modern CMOs pair up with CIOs or hire analytic talent within their teams to shift the organizations’ behavior. They study their competencies, customers and competition religiously. They use analytics to align the rest of the C-suite to look at growth factors and to reposition the company toward higher meanings, goals and aspirations.

So while many people often emphasize one trait over the other, today’s modern CMO strategically blends art, analytics and marketing religion to execute campaigns and strategies built to deliver results.

This article originally appeared in Contact Center Analytics Review.

5 Secrets of Customer Retention Marketing

Retention Marketing has become one of the most strategic marketing initiatives in the past few years. After all, it is 81% less expensive to upsell to an existing customer than it is to acquire a new one. In addition, customer retention deepens the relationship between the vendors and their clients and creates larger customer life time value in return.

But what are the secrets and best practices that marketers can put in place to create customers for life? Here are five recommendations for success. These five tips are gathered collectively from strategies employed by some of our most successful customers and their use of Birst for retention analytics.

1. Identify customer tipping points
Use analytics to understand customer life cycle. Create cohorts of customers such as new, early, expanded and mature stages, and learn customer behavior in each stage. Integrate data from product usage, customer feedback, support cases and payment information to identify early signs of churn signals.

2. Create a frequent communications calendar, programs and
loyalty incentives
A programmed sequence of letters, events, phone calls, rewards, special offers, follow-ups and magic moments creates positive energy, communicates your value to the customer again and again and reinforces the reason they’re doing business with you in the first place.

3. Align marketing with product, customer service and support teams
What you say vs. what you do has a huge impact on your company’s long-term success and customer retention. The design, quality, reliability and serviceability of your product or service must meet the standard your customers expect. Work with your product and services teams to deliver on the value that you (as a marketer) are promising to your customers.

4. Create communities
Bring your customers together. Annual user conferences are one of the most successful venues for your customers to share ideas and expand on the value they have been getting form your products. Online communities and social networks also create active forums to increase customer advocacy, trust and loyalty.

5. Sell and sell again
Contrary to popular belief, selling is not about throwing your product over the wall. Selling is about allaying your customers’ fears and being actively engaged with them in each step of the way to victory. Instead of chasing yet another sale, strike while the iron is hot. Demonstrate that you care – and do care. Create a customer success team who is on a mission to stay engaged, understand customer pains and offer solutions. That is the only way to deepen your relationships. At Birst, to do that, we follow these six principles.

There are plenty of success stories to share here, but a great example of retention analytics at work is the leading digital document management software and how Birst helps them analyze user behavior within each customer segment to identify profiles that are likely to churn. These insights have empowered their executive team to put in place new ways of mitigating risks and preventing churn.

To learn more about this use case and other marketing analytics use cases check out our “10 Ways to Put Your Marketing Data into Sharp Focus” e-book.

This article originally appeared in Digital Marketing Magazine and in the Birst blog.

The Marketing in All Things Human–From Beers and Diapers to Life Insurance and Games

Some twenty years ago, the classic example of diapers and beers became the legend that gave rise to a thriving industry: data warehousing and BI.

The folklore goes something like this:

A big retailer mined all of their customer transactions, looking for correlations that would better inform their business. To their surprise, they discovered a direct correlation between the sale of beer and diapers –mainly on Friday afternoons and to men between the ages of 25 and 35. It turns out that men were often being asked to bring home diapers for their newborns and they were picking them up on Friday nights after work. The correlation was found when these same men also picked up beer. What did the supermarket do as a result? They put the beer display next to the diapers, discounted one item but not the other. Sales shot up.

While stories like this have been used to showcase data-driven marketing, we’ve come a long way since. These scenarios were able to identify trends by grouping individuals into blended averages, but the retailer didn’t have a clear picture of the actual person they were dealing with. They were dealing with proxies, using general demographics data to underpin campaign or pricing activities. Since then, “personalization” has taken a whole new meaning and today, distinguishing and recognizing consumers as unique individuals is not only a possibility, it’s an expectation.

The new approaches to personalization and product recommendations work by discovering the relationships among activities of each customer and blending that with contextual data about the customer’s location, sentiments, or life event in order to present the most relevant product, at the exact right time. This includes analysis of historical snapshot information that follows the customer over time and predicts future purchasing behavior as well as data in real-time – for instance – from point of sale.

At Birst, we have seen this kind of product recommendation in both wealth management and gaming.

For instance – one of the largest banks in Canada is using analytics to provide insight to its financial advisors so they can make new product recommendations to their clients at critical junctures of their lives – i.e. when changes in marital status, retirement, income levels or new family members happen. By constantly monitoring changes in customer demographics and correlating the population with similar groups of the same characteristics, financial advisors are able to promote additional products (e.g. life insurance) to existing clients when the time is right and when the client is ready for that type of conversation. The results have been astonishing: the financial advisors with analytics have gathered twice as many assets under their management as the ones that did not have analytics. Since then, the bank has gone to spread the success to all its advisors and management team by putting analytics at everyone’s fingertips.

In another example, a leading children’s educational entertainment company uses Birst to measure player behavior within their products. By understanding user behavior within the application and matching that with sales data from their CRM system, they are able to effectively market new games back into their most active user populations.

Marketing is getting more personal. As analytics evolve to better leverage the data that consumers are actively contributing – such as location, life events or even health information from wearable devices – marketers will become smarter about understanding their customer as people with behaviors, emotions and unique human natures.

Companies that learn about their consumers in richer and more complete ways will gain a significant competitive advantage and find more opportunities to bridge the gap between people and the products and services they offer.

To learn more about how analytics is used by marketers today, download our new e-book.

This blog was originally posted here.

Farnaz Erfan

The Road to Success with Big Data – Expectations vs. the Reality

Big Data is complex. The technologies in Big Data are rapidly maturing, but are still in many ways in an adolescent phase. While Hadoop is dominating the charts for Big Data, in the recent years we have seen a variety of technologies born out of the early starters in this space- such as Google, Yahoo, Facebook and Cloudera. To name a few:

• MapReduce: Programming model in Java for parallel processing of large data sets in Hadoop clusters
• Pig: A high-level scripting language to create data flows from and to Hadoop
• Hive: SQL-like access for data in Hadoop
• Impala: SQL query engine that runs inside Hadoop for faster query response times

It’s clear, the spectrum of interaction and interfacing with Hadoop has matured beyond pure programming in Java into abstraction layers that look and feel like SQL. Much of this is due to the lack of resources and talent in big data – and therefore the mantra of “the more we make Big Data feel like structured data, the better adoption it will gain.”

But wait, not so fast. You can make Hadoop act like a SQL data store. However, there are consequences, as Chris Deptula from OpenBI explains in his blog, A Cautionary Tale for Becoming too Reliant on Hive. You are forgoing flexibility and speed if you choose Hive for a more complex query as opposed to pure programming or using a visual interface to MapReduce.

This goes to show that there are numerous areas of advancements in Hadoop that have yet to be achieved – in this case better performance optimization in Hive. I come from a relational world – namely DB2 – where we spent a tremendous amount of time making this high-performance transactional database – that was developed in the 70’s – even more powerful in the 2000s, and that journey continues today.

Granted, the rate of innovation is much faster today than it was 10, 20, 30 years ago, but we are not yet at the finish line with Hadoop. We need to understand the realities of what Hadoop can and cannot do today, while we forge ahead with big data innovation.

Here are a few areas of opportunity for innovation in Hadoop and strategies to fill the gap:

• High-Performance Analytics: Hadoop was never built to be a high-performance data interaction platform. Although there are newer technologies that are cracking the nut on real-time access and interactivity with Hadoop, fast analytics still need multi-dimensional cubes, in-memory and caching technology, analytic databases or a combination of them.
• Security: There are security risks within Hadoop. It would not be in your best interest to open the gates for all users to access information within Hadoop. Until this gap is closed further, a data access layer can help you extract just the right data out of Hadoop for interaction.
• APIs: Business applications have lived a long time on relational data sources. However with web, mobile and social applications, there is a need to read, write and update data in NoSQL data stores such as Hadoop. Instead of direct programming, APIs can simplify this effort for millions of developers who are building the next generation of applications.
• Data Integration, Enrichment, Quality Control and Movement: While Hadoop stands strong in storing massive amounts of unstructured / semi-structured data, it is not the only infrastructure in place in today’s data management environments. Therefore, easy integration with other data sources is critical for a long-term success.

The road to success with Hadoop is full of opportunities and obstacles and it is important to understand what is possible today and what to expect next. With all the hype around big data, it is easy to expect Hadoop to do anything and everything. However, successful companies are those that choose combination of technologies that works best for them.

What are your Hadoop expectations?

– Farnaz Erfan, Product Marketing, Pentaho

This blog was originally posted here.

Is Hadoop Knowledge a Must-Have for Today’s Big Data Scientist?

Finding data scientists and other highly technical resources that understand the complexity of big data is one of the most common roadblocks to getting value from big data. Typically, these resources need to understand Hadoop and new programming methods to read, manipulate and model big data.

As big data analytics tools advance, addressing these technologies will become less difficult, so big data scientists must master additional skills.

To make a real business impact, data scientists must have:

1. Innate analytical skills
They must have a natural curiosity for experimenting with data and often begin analysis without a clear picture of the end goal. This is a different paradigm than solving a specific, identified problem through coding or by running a query.

2. Business finesse
Sexy dashboards ultimately fail if a business doesn’t act on what the data is indicating. To succeed, data scientists must know how to translate the impact of their insights to the business.

3. Collaboration skills
Teamwork and the ability to collaborate across an organization separate those who use data to drive change from those who merely build interesting algorithms.

Big data advancements have brought technologies such as Hadoop to democratize big data to all. However, individuals skilled at data manipulation and programming in Hadoop remain scarce. Fortunately, new, innovative and easy to use big data discovery applications have broaden big data access to those without much technical skills.

So the question is: Will these new types of discovery applications for big data demand a different kind of data scientist going forward – one with analytical, interpersonal and business skills? Or would in-depth understanding of emerging technologies such as Hadoop continue to be the most important skills in ‘data scientists’?

– Farnaz Erfan, Product & Solution Marketing, Pentaho

This blog was originally posted on SmartData Collective.

How Predictive Analytics Saved Tesla?

In the last couple of weeks the feud between The NY Times Editor, John Broder – and Tesla Motors’ CEO, Elon Musk has played out in the media.

It all started when Broder took a highway trip between Washington D.C. and Boston, cruising in Tesla’s Model S luxury sedan. The purpose of the trip was to range test the car between two new supercharging stations. This 200 miles trip was well under the Model S’s 265-mile estimated range. But nonetheless the trip was filled with anxiety for Broder. Fearful of not reaching his charging destination, he had to turn off the battery-draining amenities such as radio and heater (in a 30 degree weather) to finally reach his destination – feet and knuckles “frozen”.

In rebutting Broder’s claims, Tesla’s chief executive, Elon Musk, has charged that the story was faked, that Mr. Broder intentionally caused his car to fail. On his Tesla blog, he released graphs and charts, based on driving logs that contest many of the details of Mr. Broder’s article.

With the logs now published, one thing is clear — Tesla’s use of predictive analytics helped them warn Broder on what is ahead. By calculating the range based on the energy consumption, Tesla signaled Broder to charge the vehicle in time. Had Tesla not been able to call its log files as witness, this futuristic motor tech company could have experienced serious brand damage.

What’s interesting is that Tesla’s story is not unique. Today, virtually anything that we use, an appliance, a mobile phone, an application, generates some sort of data – machine-generated data. And the truth exists behind that data. Such data, when analyzed and mined properly, provides indicators that solve problems, ahead of time.

Having real-time access to machine-generated data to foresee problems and improve performance is exactly why NetApp is using Pentaho. Using Hadoop and Pentaho Business Analytics to process and drive insights from 2-5 TBs of incoming data per week, NetApp has built a solution that sends alerts and notifications ahead of the actual hardware failure. The solution has helped NetApp predict its appliance interruptions for the E-Series storage units, offering new ways to exceed customer SLAs and protect the brand’s image.

Tesla, NetApp or other, if you run a data-driven business, the more your company can act on that data to improve your application, service or product performance, the better off your customers and the better your brand will be.

Pentaho Business Analytics gives companies fast and easy ways for collecting, analyzing and predicting data patterns. Pentaho’s customers see the value of analytics in many different facets and use cases. NetApp’s use case will be featured in Strata’s upcoming conference on Thursday, February 28, 2012.

Join us to find out more.

– Farnaz Erfan, Product and Solution Marketing, Pentaho

Looking to the Future of Business Analytics with Pentaho 4.8

Last week Pentaho announced Pentaho 4.8, another milestone in delivering the future of analytics. It has been an exciting ride. Our partners’ and our customers’ feedback have kept us ecstatic and ready to excel further into the future.

Pentaho 4.8 is a true testament on what the future of analytics needs. The future of analytics is driven by the data problems that businesses face every day – and is dependent on the information users and their expectations for solving those problems.

Let me give you a good example. I recently had the pleasure to meet with one of our customers – BeachMint. BeachMint is a fashion and style ecommerce company who uses celebrities / celebrity stylists to promote its retail business.

This rapidly growing online retailer needed to keep tabs on its large twitter and facebook communities to track customer sentiment and social influence. It then uses the social data to define customer cohorts and design marketing campaigns that best target each cohort.

For BeachMint insight to data is extremely important. But on one hand, the volumes and variety of data – in this case unstructured social data and click-through ad feeds – has increased its complexity. And on the other hand, the speed in which it gets created has accelerated rapidly. For example, in addition to analyzing the impact of customer sentiments on their purchasing behavior, BeachMint also needed to gain up-to-the-minute information on the activity of key promotional codes – to immediately identify those that leak out.

Pentaho understands these data challenges and user expectations. In this release Pentaho takes full advantage of its tightly coupled Data Integration and Business Analytics platform – to simplify data exploration, discovery and visualization for all users and all data types – and to deliver this information to users immediately – sometimes even at a micro-second level. In this release Pentaho delivers:

– Pentaho Mobile – the only Mobile BI application with the power to instantly create new analysis on the go.

– Pentaho Instaview – the industry’s first instant and interactive big data visualization application.

Want to find out more? Register for Pentaho 4.8 webinar and see for yourself.

– Farnaz Erfan, Product Marketing, Pentaho

Is Your Big Data Hot or Not?

Data is the most strategic asset for any business. However, massive volumes and variety of data has made catching it at the right time and right place, discovering what’s hot – and needs more attention – and what’s not, a bit trickier these days.

Heat grids are ideal for seeing a range of values in data as they provide a gradient scale, showing a change in data intensity through the use of colors. For example, you can see what’s hot in red and what’s normal in green; and everything else in various shades of color in between. Let me give you two examples of how companies have used heat grids to see if their data is hot or not:

Example #1 – A retailer is looking at week-by-week sales of a new fashion line to understand how each product line is performing as items get continually discounted throughout the season. Data is gathered from thousands of stores across the country and then entered into a heat grid graph that includes:

• X axis – week 1 through 12, beginning from the launch of a new campaign (e.g. Nordstrom’s Summer Looks)
• Y axis – product line (e.g. shoes, dresses, skirts, tops, accessories)
• Color of the squares – % of discount (e.g. dark red = 70%, red = 60%, orange = 50%, yellow = 30%, green = full price)
• Size of the squares – # of units sold

Looking at this graph, the retailer can easily see that most shoes sell at the beginning of the season – even without heavy discounts. This helps the retailer predict inventory levels to keep up with the demand for shoes.

It also shows that accessories almost never sell at regular prices, nor do they sell well when the discount levels are higher than 70%. Knowing this, the retailer can control its capital spending by not overstocking on this item. The retailer can also increase profit per square footage of their store by reselling its accessories earlier in the season to avoid high markdowns and inventory overstocks at the end of the season.

Example # 2 – A digital music streaming service provider is using analytics to assess the performance of its sales channels (direct vs. sales through different social media sites such as Facebook and Twitter) to guide future marketing and development spend. For that, the company uses a heat grid to map out:

• X axis – various devices (iPhone, iPad, Android Smartphone, Android Tablet, Blackberry)
• Y axis – various channels (direct site, Facebook, Twitter, …)
• Color of the circles – # of downloads (0-100 = red, 100-1000=orange, 1000-10000 = yellow, 10000+ = green)
• Size of the circles – app usage hours per day – the bigger the size, the more usage

This graph helps the music service provider analyze data from millions of records to quickly understand the popularity and usage patterns of their application on different devices, sold through different channels.

Heat grids can be use in variety of other forms, such as survey scales, product rating analysis, customer satisfaction studies, risk analysis and more. Are you are ready to find out whether your big data is hot or not? Check out this 3 minute video to learn how heat grids can help you.

Understanding buyers/users and their behavior is helping many companies including ideeli – one of the most popular online retailers – and Travian Games – top German MMO (massively multiplayer online) game publisher – gain better insight from their hottest asset – their big data!

What is your hottest business asset?

–          Farnaz Erfan, Product Marketing, Pentaho

This blog was originally posted on Smart Data Collective.

4 Questions to Ask Before You Define Your Cloud BI Strategy

These days, when it comes to enterprise software, it seems that it is all about the cloud. Some software applications such as Salesforce, Marketo, and Workday, have made quite a name for themselves in this space. Can Business Intelligence follow the same path to success? Does it make sense to house your BI in the cloud? I believe that it depends. Let’s explore why.

There are four criteria that impact the decision for a cloud vs. on-premise BI strategy.  Let’s take a look at how they affect your approach.

Question 1: Where is the data located?

Your BI Strategy should vary depending on the location of data.  If your data is distributed, some data may already be in the cloud, e.g. web data / clickstreams; and some on-premise, such as corporate data. For real-time or near real-time analytics, you need to deploy your BI as close to the source as possible. For example, when analyzing supply chain data out of an on-premise SAP system, where your database, application and infrastructure are all sitting on-premise, it is expensive and frankly impractical to move the data to the cloud before you start analyzing it.

Your data can also be geographically distributed. Unless your cloud infrastructure is co-located with your data geo zones, your BI experience can suffer from data latency and long refresh intervals.

Question 2: What are the security levels of data?

It’s important to acknowledge that data security levels are different in the cloud. You may not be able to put all your analytics outside of the company firewall. According to Cisco’s 2012 Global Cloud Networking survey, 72% of respondents cited data protection security as the top obstacle to a successful implementation of cloud services.

Question 3: What are the choice preferences of your users?

Customer preference is extremely important today. The balance of power has shifted, and users and customers are now the ones who decide whether an on-premise or a cloud deployment is suitable for them. What’s more, each customer’s maturity model is different. As an application provider or business process automation provider, you need to cater to your individual customers’ business needs.

Question 4: What operational SLAs does your Cloud BI vendor oblige you to?

Your operational SLAs can depend on cloud infrastructure providers, obliging you to service quality levels different from what you need. Pure cloud BI vendors provide their BI software over the public Internet through a utility pricing and delivery scheme. As much as this model provides an attractive alternative when resources are limited, it’s not for everyone. In most cases, the SaaS BI vendor depends on IaaS vendors (such as Amazon, Savvis, OpSource, etc.) for storage, hardware, and networks. As a result, the SaaS BI vendors’ operational processes have to align with the infrastructure vendors’ for housing, running, and backup/recovery of the BI software. Depending on your BI strategy, these nested and complex SLAs may or may not be the right choice.

Large enterprises, or even mid-market companies inspired by growth, typically develop an IT strategy that is provider-agnostic and has the flexibility to be hosted on-premise or in the in the cloud.   This strategy helps companies avoid lock-in and inflexibility down the road.

As cloud technology remains one of the hottest trends in IT today, it is important to assess whether cloud is the right choice for BI. The reality is that it depends. The center of gravity for BI is still on premise; however, it will move to the cloud over time mostly through the embedded BI capabilities of enterprise SaaS applications. Successful organizations will be the ones that can navigate the boundary between the two strategies and provide greater flexibility and choice by offering a product that can be deployed on-premise, in the cloud, or a hybrid of both.

What is your Business Intelligence Cloud strategy?

— Farnaz Erfan, Product Marketing, Pentaho

This blog was originally posted on Smart Data Collective.