Campaign Management

8月 192019
 

In parts one and two of this blog series, we introduced the automation of AI (i.e., artificial intelligence) and natural language explanations applied to segmentation and marketing. Following this, we began marching down the path of practitioner-oriented examples, making the case for why we need it and where it applies. [...]

SAS Customer Intelligence 360: Automated AI and segmentation [Part 3] was published on Customer Intelligence Blog.

8月 122019
 

In part one of this blog series, we introduced the automation of AI (i.e., artificial intelligence) as a multifaceted and evolving topic for marketing and segmentation. After a discussion on maximizing the potential of a brand's first-party data, a machine learning method incorporating natural language explanations was provided in the context [...]

SAS Customer Intelligence 360: Automated AI and segmentation [Part 2] was published on Customer Intelligence Blog.

12月 052016
 

A common practice in traditional marketing is to first choose a target market to focus on. You then align your organization’s strategies and messaging to create a campaign in that target market. But what happens when it becomes clear that the campaign you created isn’t working? How agile are you in terms of adjusting on the fly and adapting to the needs of your prospective customers?

The challenge

A campaign we ran at SAS targeted small to medium-sized businesses, or SMBs. We needed to come up with tailor-made messaging that would be distinct from similar campaigns we were launching targeted at larger, enterprise-level companies. To do that, we highlighted what we thought wedata-analysisre business needs, language and case studies that would resonate with the SMBs.

But after the program launched and began, the results were disappointing. We saw lower-than-expected results for performance metrics including click-through rates and conversions. So we tweaked the messaging, offers and program structure to improve results. After crunching those numbers, the results came in – the campaign was still floundering.

We were now forced to take a fresh look. What had we done wrong? On reflection, we came upon an even more telling question: Did we actually need to separate SMBs from larger organizations? We started with an underlying assumption that the SMB market should be treated differently. Had that been a mistake?

The approach

To help guide us forward, we selected a roster of key performance metrics to analyze:

  • E-mails sent.
  • Open rates.
  • Click-through rates.
  • Opt-out rates.
  • Conversions (those who filled out registration forms to receive the promoted asset).
  • Lead-generated SSOs (an internal measure of conversions that we identify as leads that later progress to become sales opportunities).
  • Rate of completed leads to SSOs.

We then looked at how the SMBs responded to the SMB-specific campaign compared to how they responded when they received the enterprise-level messaging.

The results

To our surprise, SMBs responded more strongly to the enterprise-level campaign (see the table below). Our assumption had been proved wrong. So we adjusted by closing the SMB-specific campaign and retargeted the SMBs with our enterprise-level messaging.

adele-table

The takeaway for us was a reminder that we can’t afford to let our assumptions about the market hinder our ability to adjust to customers’ needs. In this situation, we relied on the power of analytics to provide the answers about what people wanted rather than continue in a losing cause.

You can best meet customers along their decision journey by relying on advanced analytics to increase the quality of a marketing campaign by using scoring, optimization and predictive capabilities. The standard spreadsheet-based reports that marketers used to rely on to see how their campaign performed have now shifted to interactive visualization dashboards to track the efficacy of their campaign, while making changes on the fly when necessary to ensure a campaign is reaching its potential. The biggest difference is that marketers now have these tools at their disposal. We no longer have to submit requests to the IT department to get this information.

==

Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.

tags: Campaign Management, customer analytics, customer insights, customer journey, marketing campaigns, midmarket, smb

How analytics empowers campaign agility was published on Customer Intelligence.

12月 052016
 

A common practice in traditional marketing is to first choose a target market to focus on. You then align your organization’s strategies and messaging to create a campaign in that target market. But what happens when it becomes clear that the campaign you created isn’t working? How agile are you in terms of adjusting on the fly and adapting to the needs of your prospective customers?

The challenge

A campaign we ran at SAS targeted small to medium-sized businesses, or SMBs. We needed to come up with tailor-made messaging that would be distinct from similar campaigns we were launching targeted at larger, enterprise-level companies. To do that, we highlighted what we thought wedata-analysisre business needs, language and case studies that would resonate with the SMBs.

But after the program launched and began, the results were disappointing. We saw lower-than-expected results for performance metrics including click-through rates and conversions. So we tweaked the messaging, offers and program structure to improve results. After crunching those numbers, the results came in – the campaign was still floundering.

We were now forced to take a fresh look. What had we done wrong? On reflection, we came upon an even more telling question: Did we actually need to separate SMBs from larger organizations? We started with an underlying assumption that the SMB market should be treated differently. Had that been a mistake?

The approach

To help guide us forward, we selected a roster of key performance metrics to analyze:

  • E-mails sent.
  • Open rates.
  • Click-through rates.
  • Opt-out rates.
  • Conversions (those who filled out registration forms to receive the promoted asset).
  • Lead-generated SSOs (an internal measure of conversions that we identify as leads that later progress to become sales opportunities).
  • Rate of completed leads to SSOs.

We then looked at how the SMBs responded to the SMB-specific campaign compared to how they responded when they received the enterprise-level messaging.

The results

To our surprise, SMBs responded more strongly to the enterprise-level campaign (see the table below). Our assumption had been proved wrong. So we adjusted by closing the SMB-specific campaign and retargeted the SMBs with our enterprise-level messaging.

adele-table

The takeaway for us was a reminder that we can’t afford to let our assumptions about the market hinder our ability to adjust to customers’ needs. In this situation, we relied on the power of analytics to provide the answers about what people wanted rather than continue in a losing cause.

You can best meet customers along their decision journey by relying on advanced analytics to increase the quality of a marketing campaign by using scoring, optimization and predictive capabilities. The standard spreadsheet-based reports that marketers used to rely on to see how their campaign performed have now shifted to interactive visualization dashboards to track the efficacy of their campaign, while making changes on the fly when necessary to ensure a campaign is reaching its potential. The biggest difference is that marketers now have these tools at their disposal. We no longer have to submit requests to the IT department to get this information.

==

Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.

tags: Campaign Management, customer analytics, customer insights, customer journey, marketing campaigns, midmarket, smb

How analytics empowers campaign agility was published on Customer Intelligence.

12月 052016
 

A common practice in traditional marketing is to first choose a target market to focus on. You then align your organization’s strategies and messaging to create a campaign in that target market. But what happens when it becomes clear that the campaign you created isn’t working? How agile are you in terms of adjusting on the fly and adapting to the needs of your prospective customers?

The challenge

A campaign we ran at SAS targeted small to medium-sized businesses, or SMBs. We needed to come up with tailor-made messaging that would be distinct from similar campaigns we were launching targeted at larger, enterprise-level companies. To do that, we highlighted what we thought wedata-analysisre business needs, language and case studies that would resonate with the SMBs.

But after the program launched and began, the results were disappointing. We saw lower-than-expected results for performance metrics including click-through rates and conversions. So we tweaked the messaging, offers and program structure to improve results. After crunching those numbers, the results came in – the campaign was still floundering.

We were now forced to take a fresh look. What had we done wrong? On reflection, we came upon an even more telling question: Did we actually need to separate SMBs from larger organizations? We started with an underlying assumption that the SMB market should be treated differently. Had that been a mistake?

The approach

To help guide us forward, we selected a roster of key performance metrics to analyze:

  • E-mails sent.
  • Open rates.
  • Click-through rates.
  • Opt-out rates.
  • Conversions (those who filled out registration forms to receive the promoted asset).
  • Lead-generated SSOs (an internal measure of conversions that we identify as leads that later progress to become sales opportunities).
  • Rate of completed leads to SSOs.

We then looked at how the SMBs responded to the SMB-specific campaign compared to how they responded when they received the enterprise-level messaging.

The results

To our surprise, SMBs responded more strongly to the enterprise-level campaign (see the table below). Our assumption had been proved wrong. So we adjusted by closing the SMB-specific campaign and retargeted the SMBs with our enterprise-level messaging.

adele-table

The takeaway for us was a reminder that we can’t afford to let our assumptions about the market hinder our ability to adjust to customers’ needs. In this situation, we relied on the power of analytics to provide the answers about what people wanted rather than continue in a losing cause.

You can best meet customers along their decision journey by relying on advanced analytics to increase the quality of a marketing campaign by using scoring, optimization and predictive capabilities. The standard spreadsheet-based reports that marketers used to rely on to see how their campaign performed have now shifted to interactive visualization dashboards to track the efficacy of their campaign, while making changes on the fly when necessary to ensure a campaign is reaching its potential. The biggest difference is that marketers now have these tools at their disposal. We no longer have to submit requests to the IT department to get this information.

==

Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.

tags: Campaign Management, customer analytics, customer insights, customer journey, marketing campaigns, midmarket, smb

How analytics empowers campaign agility was published on Customer Intelligence.

8月 272016
 

For the uninitiated, SAS 360 Engage enables organizations to interact with consumers by allowing them to create, manage and deliver digital content over web and mobile channels.  Wait a minute. SAS does more than the analytics? That is correct. SAS 360 Engage is a marketing super force serving as a one-stop shop for data capture all the way through delivering highly-targeted, personalized digital experiences.

360 Engage 1

Being able to dynamically place content and offers into digital channels – across devices and points in time – is nothing new for savvy marketing brands focused on optimization. As customer journeys spread across fragmented touch points while customers are demanding seamless and relevant experiences, content-oriented marketers have been forced to reevaluate their strategies for engagement. But the complexity, pace and volume of modern marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.

Presently, marketers primarily use a variety of content optimization approaches that include A/B testing and multivariate testing. A/B testing, at its simplest, is a method of website or mobile optimization in which the conversion rates of two versions of a page are compared using visitor response rates. By tracking the way visitors interact with the content– the videos they watch, the buttons they click, or whether they sign up for a newsletter – you can infer which version of the content is most effective.

Due to the popularity of this technique, SAS 360 Engage supports A/B/n testing.  A/B/n testing is an extension of A/B testing, where “N” refers to the number of versions being tested, anywhere from two versions to the “nth” version. For example, when a brand has more than one idea for what the ideal digital experience should be, A/B/n can be used to compare each hypothesis and produce an optimized decision based on data, not subjectivity.

360 Engage 2

Testing is attractive because it is efficient, measurable and serves as a machete cutting through the noise and assumptions associated with delivering effective experiences. In parallel, the evolving marketing landscape is driving a greater mandate for testing: to operate in more channels, handle more data and support more users. Testing must mature beyond traditional on-site experimentation to fully optimize a multifaceted customer journey.

360 Engage 3

The majority of today’s technologies for personalization have generally failed to effectively use data science to offer consumers a contextualized digital experience. Many of today’s offerings are based on simple rules-based segmentation to drive recommendations. Building off the benefits of multi-channel A/B/n testing, this is where SAS 360 Engage injects its analytical muscle to differentiate from other personalization technologies.  Let's break this down:

  • At the conclusion of an A/B/n test, there is usually a winner and one or more losers.
  • Is there really one superior experience for your entire marketable audience? Is it possible that experiences should vary by segment?

Performing algorithmic segmentation sounds awesome, but who really has the time to do it? We have so many tests to run.360 Engage 4

360 Engage 5

360 Engage 6

The time has arrived for predictive marketing to have its moment in the sun, and with Forrester recently naming SAS the leader in customer analytics, it's official - the 800-pound gorilla in advanced analytics is locked in on solving complex issues facing the space of data-driven marketing. Making digital personalization more relevant for target audiences is just like preparing a delicious meal; it all comes down to the ingredients and preparation process to rise to the occasion!

A beautiful and interpretable visualization is generated highlighting what is unique about this segment, as compared to everyone else who was exposed to the test. If the brand wants to target this audience in future campaigns, a single click populates this segment in the platform for future journey orchestration.

If you look closely at the image, you will note in the upper half of the report that the winner of the A/B/n test is variant A. However, the lower half of the report showcases a newly discovered segment. It turns out that when a specific customer segment with recent purchase, stay and amenity activity interacts with this hospitality brand, variant B produces better results. How did SAS 360 Engage do this? By applying automated firepower (i.e. algorithmic clustering) to produce this prescriptive and actionable insight. To learn more about this segment, marketers can profile the audience:

SAS 360 Engage was built with the recognition that some marketing teams don't have data scientists available, and have real needs for analytical automation. To improve upon the concept of A/B/n testing, augmenting this capability with automated, algorithmic segmentation with prescriptive results addresses an important need. Let's assume you've run an A/B/n test with four versions of a page, and variant A was crowned the champion. Wouldn't it be nice to know that if a specific segment arrived at your website, an alternative experience would facilitate a better result?

tags: A/B Testing, Campaign Management, customer journey, data science, digital marketing, Digital Personalization, marketing analytics, predictive analytics, Predictive Marketing, Prescriptive Analytics, SAS 360 Engage, SAS Customer Intelligence 360, segmentation

SAS 360 Engage: A/B testing and algorithmic segmentation was published on Customer Intelligence.

4月 242015
 

A strong campaign management platform is just one of the ingredients needed to allow brands to provide contextual customer experiences across a plethora of channels. And for the ninth consecutive year, Gartner has named SAS a “Leader” in its Magic Quadrant for Multichannel Campaign Management based on completeness of vision and ability to execute.

Gartner named SAS a leader in campaign management the 9th year in a row!SAS® Customer Intelligence helps organizations get in sync with the customer journey by enabling them to combine direct and indirect communication channels – websites, stores, catalogs, direct mail, email, mobile, etc. – and take action using the customers’ channel of choice. And continuous improvements are what keep SAS a leader in this area.

Recent updates to SAS Customer Intelligence improve offer staging and real-time arbitration. With consumers increasingly demanding faster service, this helps brands deliver immediate and appropriate response across digital and traditional channels. Supplying a next best offer or action to a consumer – one that is anticipated and contextual – is the Holy Grail for brands striving to advance their marketing, service and support.

Providing this organic and refined next-best-action capability positions SAS as the analytically based, decision-making “brain” within enterprise marketing today.

Other new features include:

  • Improved “next best offer” management. Marketers can set offers to be arbitrated at the individual customer level, easily deliver consistency across all channels and centrally set customer eligibility criteria as part of each individual offer.
  • Real-time “optimization” score code generation. Marketers can optimize unseen customers (those not contained in a group of campaigns or decisions) and reuse and deploy the score code in real time in both native and third party applications
  • Enhanced campaign management and deployment across environments. Analysts can update inbound and outbound campaigns in real time, without taking campaigns out of production.

Gartner says “leaders consistently do considerably better in overall campaign management performance for basic and advanced campaigns, and for integration with digital marketing. They have high market visibility, high market penetration, strong market momentum and a strategic vision for growing the campaign management business.”

Analyst firm evaluations like these include an assessment of existing customers using SAS Customer Intelligence for powerful, analytically-driven marketing. And we're very grateful for our many loyal fans and customers. If you're not currently using SAS Customer Intelligence in your marketing, do let us know.

We've earned our stripes as a leader and we can help make you a leader, too.

tags: Analyst Validation, Campaign Management, Gartner, multichannel

The post SAS a leader in the Gartner Campaign Management Magic Quadrant, again! appeared first on Customer Analytics.

2月 232015
 

Many retailers do not engage in effective forecasting.Retailers are always trying to get closer to customers. But it’s not just about improving service to those customers – it’s about understand more about what products they are demanding so as to make better forecasting decisions around, for example, how much of a particular item is needed in stock.

As this video reminded me, knowing your customers really well means you can easily know what products they will want, when and how, perhaps better than they do. Today, that knowledge is gleaned from a complex assessment of lots of online and offline data which contributes to every single buying decision by your customers.

The online element means it’s not just about attracting customers into the store. Visiting New York for the annual retail conference NRF a few weeks ago, I thought about how the value of retail space has changed. Retail giants that have been there for years, as well as the new retailers on the block, are continuing to transform the experience in-store to stay ahead of customer demand. We are enticed in-store with Wi-Fi, coffee, beer, or live DJs. But this only gets a retailer so far if they cannot capture key data about customers coming in to their stores.

The main problem, however, is not one of getting hold of data; it’s being able to forecast using that data.

We recently conducted the 3rd Annual Analytics in Retail Study, which reports that 71 per cent of retailers performed either basic or no reporting at all when it came to forecasting customer trends. Whilst a large proportion had the ability to gather data, and many were doing so, there is a clear gap in ability to analyse this data to inform business strategy. There are many possible reasons for this; a lack of in-house skills, and/or a lack of awareness of available technology that is more accessible.

At NRF it seemed clear to me that most retailers are continuing to find forecasting a problem, as they are also struggling against the volatility of promotions on stock and the lack of good data around non-traditional channels. As the view of the high street continues to change, shifting that ability to forecast for future buying trends will be the next frontier for retailers in 2015.

More and more retailers are looking at ways of capturing customer data in-store. Apple CEO Tim Cook recently declared 2015 to be the year of Apple Pay, which already makes up more than $2 out of $3 spent on purchases using contactless payment across the three major US card networks. We heard at NRF that this will be a focus for some of the UK's largest retailers. It provides an excellent opportunity to engage customers and enable them to pre-order items. Applying analytics to this data should help develop deeper customer relationships, and more personalised offers and prices. Crucially it also offers insight into customer demand so retailers can make better forecasting decisions.

You may also be interested in my colleague Alison Bolen’s take on the three big trends to come out of NRF this year. Read more about those here. In the meantime, let me know what you think. And thank you for following!

tags: #NRF15, Campaign Management, Inbound marketing, marketing optimization, real-time decisioning, retail