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B2B companies are leveraging advanced analytics. Analytics can help companies to customize buying experiences through ecommerce channels and to provide sales reps with guidance about what customers are likely to purchase and what prices make sense to quote in the context of the deal. Customers want their buying experience to be finely tuned to their unique needs from a product perspective as well as from a pricing perspective. Across channels, whether on the web or in sales rep interactions, B2B customers expect companies to be able to anticipate their needs.
For B2B companies, transaction data holds a wealth of information about where and how to sell more for more, but much of this information goes undetected. Algorithms and predictive models are needed to do the heavy lifting to turn this data into actionable information about each customer for sales reps. This information, or prescriptive guidance, enables sales reps to have more strategic conversations with customers and, in turn, builds the sales team’s confidence in advanced analytics. This same actionable information also can easily be fed into ecommerce platforms to provide a personalized web buying experience for customers.
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Let’s take a look at 3 practical ways to make advanced machine learning personalization work for everyday marketing:
1. Simplify your system
Take comfort in outsourcing this process to the data science professionals. There are tons of companies out there to meet your unique needs — from e-commerce product optimization, to predictive content for media and content companies, to B2B business intelligence systems.
2. Simplify your integration
Look to leverage APIs and tools that help you forget all the noise behind the scenes, and allow you to implement predictive recommendations across channels seamlessly.
3. Simplify your data analytics
One thing we as society haven’t gotten quite used to is putting our full trust into the machines. That needs to change. In order to receive the best insights and best predictive content for users, we need to give over as much control as possible in this one area.
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Digest...
The new service from Salesforce, whose mobile app is already available for iOS, amounts to a major step forward for the enterprise software, not only because it expands the company’s reach beyond the sales, marketing, and service software categories, but also because the release changes the dynamics of Salesforce’s relationships with partners that have offered business-intelligence (BI) or data analytics capabilities in the past. Some are formally announcing partnerships; others aren’t. In any case, it’s a big day in data world. Salesforce is announcing that it’s teaming up with several early-stage companies in the predictive-analytics market: 6Sense, C9, Fliptop, Gainsight, Lattice Engines, Predixion, and Wise.io. The choice of partnering rather than baking in predictive capability shows Salesforce doesn’t want to completely turn off its ecosystem of companies that offer to crunch data from Salesforce and assist sales people in figuring out which leads to focus on. The standalone Wave service costs $250 per month for each person who imports data sets — “builders,” in Salesforce parlance — and $125 per month for each person working with available data, or “explorers.” The Wave mobile app comes free of charge. ___________________________________ ► Receive a FREE daily summary of The Marketing Technology Alert directly to your inbox. To subscribe, please go to http://ineomarketing.com/About_The_MAR_Sub.html (your privacy is protected).
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The tool, which is part of the Demandbase B2B Marketing Cloud that launched earlier this year, tracks campaign performance over time for any account, industry or business segment to reveal relevant sales trends. The most highly touted features of the Sales Accelerator are predictive capabilities. It can pinpoint new opportunities and send alerts to salespeople when the activity from a key account has significantly increased. These alerts, as well as other notifications, can be sent directly through a user's Salesforce account on their mobile phone. Demandbase said that the tool can shorten the sales cycle and boost marketing investment by helping sales identify good leads before they are contacted. ___________________________________ ► Receive a FREE daily summary of The Marketing Technology Alert directly to your inbox. To subscribe, please go to http://ineomarketing.com/About_The_MAR_Sub.html (your privacy is protected).
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Digest...
To prioritize pipeline to find great customers, marketers need a deep understanding of how to market to the right customers. Marketers have access to new data sources, superior analytics, and better integrations that allow them to generate high value pipelines. The transition starts with understanding two key changes in the marketing ecosystem: (1) The explosion of data and (2) The inadequacy of disparate tools. 1. The Explosion of Data The value of big data doesn’t stem from the volume of data you aggregate, but rather, from the relationships between the data. To extract insights from sets of data, data scientists have to take the volume, velocity, variety, and veracity of data into account; they don’t just experiment on data, they merge, organize, clean, and test data as well. One tool might include Predictive Lead Scoring and another might be hiring a team of analysts that require extensive training. 2. Ineffective, Disparate Tools According to Bizo ’s survey of over 800 B2B marketing leaders, less than 20 percent believe they are using data well. And for marketers targeting SMBs, the data problem is even worse. Only 8 percent of marketers have a 360-degree view of their SMB customers. Given the universal challenges facing marketers today, specific to big data and customer insight, this is not necessarily surprising. __________________ ► Receive a FREE daily summary of The Marketing Technology Alert directly to your inbox. To subscribe, please go to http://ineomarketing.com/About_The_MAR_Sub.html (your privacy is protected).
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Digest...
Salesforce.com, already a big dealer of enterprise software, looks set to launch a whole new product line at its Dreamforce conference next month. The name: Analytics Cloud. Here’s the evidence. Look at the the Oct. 15 column of the “draft agenda” Salesforce chief executive Marc Benioff tweeted out this morning. The highest green box in that column says “Analytics Cloud Keynote.” Whoa. It’s a big deal for Salesforce to finally do more to analyze data, and likely the data already sitting in Salesforce software for tracking sales leads, help-desk requests, and marketing-automation tools. Think business intelligence, think business analytics, which lots of companies, big and small, can do for Salesforce data. Which could make the move a big deal for companies in the Salesforce ecosystem. __________________ ► Receive a FREE daily summary of The Marketing Technology Alert directly to your inbox. To subscribe, please go to http://ineomarketing.com/About_The_MAR_Sub.html (your privacy is protected).
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Digest...
Of course, best-in-class analytics today are not the same as they were ten, or even five, years ago. Back then you could develop predictive models with static data from your data warehouse and use the scoring output to develop complex rules-based systems or segmentation lists. The process would easily take six months to deliver value. Those days are long gone. Now, a wider breadth of data flows around the clock, the traditional role of the middleman is being eliminated, and new forms of intermediaries are making informed decisions at lightning speed. Today, the machine-learning algorithms we deploy must run against flowing data streams and adapt over time to changes in market dynamics and consumer behavior. Subtle changes in the customer buying process are buried in the data, and we can fish them out — in near-real time. In the marketing world, this is exciting stuff! The use of data science to produce signal-based treatments is revolutionary on a very broad scale. First, we now have unprecedented amounts of accurate, predictive information. Second, analytics are now focused on individual behaviors, not demographics or macro-segments. Third, the extraction and application of real-time predictive signals gives marketers entirely new ways to manage customer relationships. It's at this point, when advanced data science can completely reinvent the customer experience, that marketing automation makes sense to me. __________________ ► Receive a FREE daily summary of The Marketing Technology Alert directly to your inbox. To subscribe, please go to http://ineomarketing.com/About_The_MAR_Sub.html (your privacy is protected).
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Advanced/ Digest...
Prediction #1: Beyond Dashboards [Marketers] will ask data specific questions for the insights to make time-sensitive business decisions. Why is this so important? Current-state reporting tells you what has already happened, but does not reveal what the marketer really needs: insights into why consumers make the buying decisions they do. Advances in analytics technology AND marketers' acceptance of it will deliver data not just on the "what," but also the "why," the "when" and the "what if" for true decision support. Prediction #2: Right-Time Marketing Right-time marketing will become more and more common as businesses are able to combine and make sense out of consumer breadcrumbs across all channels of engagement in a timely manner. This next-gen approach, however, will require access to real-time, granular multichannel data across all channels, which can then be analyzed in real time to reveal hidden relationships and shed new insights into critical business decisions. Prediction #3: Marketers as the New "Quants" As marketers begin to understand their data and begin to predict behavior, they will be more like the "quants" on Wall Street with automated trading algorithms - but with one caveat. New tools will enable not just the "data scientist," but also the average marketer to easily create forecasts, determine optimal approach, automate all necessary consumer-facing actions, observe actual outcome, learn and repeat for improved results! ___________________________________ ► Receive a FREE daily summary of The Marketing Technology Alert directly to your inbox. To subscribe, please go to http://ineomarketing.com/About_The_MAR_Sub.html (your privacy is protected).
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Segmentation - Yes, buyers leave breadcrumbs behind them, in the form of data, with every online click or in-store interaction. But mountains of data don't solve a core problem: How do you ...
Key excerpt...
To increase campaign relevancy and effectiveness, you need to discover the direct and indirect relationships among traditional and digital touchpoints. By analyzing those relationships, marketers can target the most valuable customers for specific products or categories in terms of best potential for purchase, lifetime value, retention, or highest viral factor.
Here are the three key steps... 1. Segment and analyze Capture real-time, granular website data on response, engagement, and conversion rates from each social and paid campaign. Segment and analyze those data points in real time to identify leading indicators of success. Rapidly assess campaign effectiveness and optimize ad messages, content marketing, or social media tactics on the fly. 2. Contextualize and enrich Contextualize and enrich real-time data with sales, revenue, and CRM actuals, as well as reference data sources to validate and improve the accuracy of the optimization models. Discover additional relationships that complete buyer segment profiles to improve targeting. 3. Predict and prescribe Combine real-time and historical data to create an enhanced foundation for running predictive models that reveal the likelihood of future outcomes. And remember, you don't just forecast what might happen. You want the power to create the future you want. That requires access to highly granular data identifying what campaigns and promotions lead to purchases of specific products by buyer segments in a given season.
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For all marketers, marketing data analytics is essential to predicting customer behavior. However, only a small percentage of marketers are creating and testing hypotheses.
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AKA using advanced analytics to be proactive.
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