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What is a Customer Data Platform ( CDP )

CDP Institute says a customer data platform is packaged software that creates a persistent, unified customer database that is accessible to other systems.
According to prestigious publications like Forbes, companies that give priority to data-driven marketing are six times more profitable year-over-year than companies that don’t. Seventy-five percent of those data-driven companies also see an increase in customer engagement, and data-driven strategies often produce between five to eight times more ROI than strategies without.
Results like this support the idea that the future of marketing lives in the data, but not just any data- it’s a combination of several components. Its behavioral data enhanced by AI (artificial intelligence) and machine learning is accurate, real-time, and interconnected.

How do enterprise-level marketers drive real business results?
By getting hold of and using actionable, real-time, accurate behavioral data. Companies that do, dominate in their respective industries. Examples of data-driven companies that embrace a customer-driven platform are Amazon, Spotify, and Netflix.

What is a customer-driven platform?
Defined by the CDP Institute, a customer-driven platform is packaged software that creates a persistent, unified customer database that is accessible to other systems.

At the elementary level, the customer-driven platform stores, connect, and combines customer data to give a real-time, 360-view of customers regardless of which devices or channels they interact with. It also supports customer acquisition and the “customer journey.”

All the data is captured if shopper “X” follows your company on social media, or browses your website, or subscribes via email, or uses your customer service ticketing systems. Their behavior such as the content they prefer to interact with, the emails they open, or customer service interaction are all stored and updated and available in real-time.

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When you get a timely, well-planned call to an inbound lead, it’s far more efficient than any volume of cold calling. You know specifically what the prospect is researching based their current browsing behaviour — plus, you have sufficient data about the prospect to do some initial analysis about the business they work for and their role within the company. 5. Send personalized emails. Email marketing remains to be a highly efficient strategy for lead nurturing — and the personalization of those emails tends to provide better results. Research by Accenture found that 41% of customers turned businesses due to a lack of personalization. There are several ways to personalize emails to increase your lead nurturing tactics. You can send triggered emails when a visitant performs an action like downloads your gated content, visits certain pages on your website, clicks on links in your emails, or demonstrates a high level of engagement. When you combine the skill of marketing personalization with behaviorally triggered emails you can deliver the right marketing messages to the right audience, at accurately the right times. 6. Use lead scoring strategies. For those who are new to the concept of lead scoring, this methodology is utilised to place prospects on a scale that describes the observed value each lead represents to the business. Lead scoring can be performed in many marketing automation platforms by allowing numeric values to specific website browsing behaviours, conversion events, or even social media synergies. The resulting score is utilised to decide which leads should be followed up with immediately by a sales rep and which leads need to be nurtured more. 7. Align your sales and marketing strategies. When sales and marketing align, lead nurturing tactics are more prosperous and consumer retention rates increase. For both sales and marketing to provide to lead nurturing recognise the exact points in the buyer's journey that prospects should be transitioned between duos — to do so, consider various triggers like workflow enrollment, lead scoring, and conversion events. The shared expectations, abilities, and goals for this cross-team collaboration should be described in a sales and marketing service level agreement (SLA). Designing a SLA will help the two teams hold each other responsible for turning leads and efficiently nurturing them into paying consumers. https://wwwaioziumcom/
How Machine Learning Is Changing IT Monitoring In 2020
The IT infrastructure has become remarkably complex; it becomes crucial for IT leaders to create new monitoring processes relevant to their organizations. IT monitoring covers a wide range of products allowing analysts to determine if the IT team performs at the expected level of service and manage any problems detected. This can be done by basic testing or using advanced tools like machine learning (ML). As the speed of change in the industry increases, IT operations are required to help the business stay afloat to fill experience gaps and allow customers to focus on their business. The challenge that the IT monitoring team facing is the tendency to use legacy systems that need to be actively running. This puts the IT monitoring team at a significant disadvantage and leaves them scrutinizing unnecessary noise and missing information packets. What if the performance of these systems is optimized? Artificial intelligence (AI) and machine learning (ML) continue to play a vital role in taking the pressure off internal processes. The road to leverage AI and ML are partly driven by the need to implement data first when building core systems, partly because of the cross-industry leap to cloud. In such crises as COVID19, companies are trying to capitalize on the power of AI-powered tools, and more organizations are creating pathways that reflect the need for strategic change. Machine learning in IT monitoring # 1 | Adjusted alerts Sharpening the known pain point in traditional anomaly detection systems, using a combination of supervised and unsupervised machine learning algorithms, we can reduce the signal-to-noise ratio of alarms as well as correlate those alerts across multiple toolkits in real-time. Additionally, algorithms can capture corrective behavior to suggest remedial steps when a problem occurs in the future. # 2 | Comparing the indicators We can determine correlations between metrics sent from different data sources in our infrastructure and applications through advanced anomaly detection systems based on machine learning algorithms. Additionally, some ML platforms provide one-time cost optimization reports that can compare instance usage to AWS spend. # 3 | Business Intelligence Different anomalies can be detected within massive amounts of data to turn them into valuable business insights via real-time analytics and automated irregular detection systems. Machine learning logic can be applied to metrics obtained from various sources to perform automated anomaly detection before processing the data to mark anomalies that can be scored to be used for identifying how much irregularity the event is. # 4 | Natural language processing Machine learning helps define millions of events into a single manageable set of insights using topology, semantic, natural language processing, and clustering algorithms. Similar to the previous solutions, using these algorithms helps reduce the triggered events and alerts, which allow more efficient use of resources and faster problem resolution. # 5 | Cognitive perception There is an alternative use of machine learning for IT monitoring to combine ML with crowdsourcing to filter out massive log data to identify events. This helps focus on how humans interact with the data rather than focus solely on mathematical analysis. This approach is called perceptual insights, and it denotes important events that may occur, and that needs to be taken into account. Although the application of machine learning is not strictly straightforward, its potential is clear to transform IT monitoring. As IT infrastructure continues to grow, it is clear that many industries are turning to ML to find effective and budget-friendly solutions today and in the future. One side note Vietnam software outsourcing industry has recently become dynamic. When it comes to Vietnam Machine Learning engineers, they are well equipped with the necessary knowledge and skillsets.