In our prior blog, we discussed a definition and framework for Digital Twins consistent with how our customers are using Digital Twins in their applications. We defined Digital Twin as “a living digital representation of an individual physical system that is dynamically updated with data to mimic the true structure, state, and behavior of the physical system, to drive business outcomes.” In addition, we described a four-level Digital Twin leveling index, shown in the figure below, to help customers understand their use-cases and the technologies needed to achieve the business value they are seeking.

In this blog, we will illustrate how the L3 Predictive level predicts behavior of a physical system by walking through an example of an electric vehicle (EV). You will learn, through the example use-cases, about the data, models, technologies, AWS services, and business processes needed to create and support an L3 Predictive Digital Twin solution. In prior blogs, we described the L1 Descriptive and L2 Informative levels, and a future blog, we will continue with the same EV example to demonstrate L4 Living Digital Twins.

L3 Predictive Digital Twin

An L3 Digital Twin focuses on modeling the behavior of the physical system to make predictions of unmeasured quantities or future states under continued operations with the assumption that future behavior is the same as the past. This assumption is reasonably valid for short-time horizons looking forward. The predictive models can be machine learning based, first-principles based (e.g. physics simulations), or a hybrid. To illustrate L3 Predictive Digital Twins, we will continue our example of the electric vehicle (EV) from the L1 Descriptive and L2 Informative Digital Twin blogs by focusing on three use cases: 1/ virtual sensors; 2/ anomaly detection; and 3/ imminent failure predictions over very short time horizons. To illustrate how to implement on AWS, we have extended our AWS IoT TwinMaker example from the L2 Informative blog with components related to these three capabilities. In the next sections we will discuss each of them individually.

1. Virtual Sensor

For our EV example, a common challenge is to estimate the remaining range of the vehicle given its battery’s present state of charge (SoC). For the driver, this is a critical piece of information since getting stranded generally requires having your EV towed to the nearest charging station. Predicting the remaining range, however, is not trivial as it requires implementing a model that takes into account the battery state of charge, the battery discharge characteristics, the ambient temperature which has an impact on battery performance, as well as some assumptions on the expected upcoming driving profile (e.g., flat or mountainous terrain, defensive or aggressive accelerations). In our L2 Informative blog, we used a very crude calculation for Remaining Range that could easily be hardcoded into an embedded controller. In our L3 Predictive example below, we replaced the simple calculation with an extension of the EV simulation model provided by our AWS Partner Maplesoft in our L1 Descriptive blog. This time the model incorporates a virtual sensor that calculates the estimated range based on the key input factors described above. The virtual sensor based vehicle range is shown in the Grafana dashboard below.

2. Anomaly Detection

With industrial equipment, a common use case is to detect when the equipment is running off-nominal performance. This type of anomaly detection is often integrated directly into the control system using simple rules such as threshold exceedances (e.g., temperature exceeds 100°C), or more complex statistical process control methods. These types of rules-based approaches would be incorporated into L2 Informative use cases. In practice, detecting off-nominal performance in a complex system like an EV is challenging, because the expected performance of a single component is dependent on the overall system operation. For example, for an EV, the battery discharge is expected to be much greater during a hard acceleration compared to driving at constant speed. Using a simple rules-based threshold on the battery discharge rate wouldn’t work because the system would think that every hard acceleration is an anomalous battery event. Over the past 15 years, we’ve seen increased use of machine learning methods for anomaly detection by first characterizing normal behavior based on historical data streams, and then constantly monitoring the real time data streams for deviations from the normal behavior. Amazon Lookout for Equipment is a managed service that deploys supervised and unsupervised machine learning methods to perform this type of anomaly detection. The figure below shows a screenshot from the Grafana dashboard showing that the “Check Battery” light has been illuminated due to anomalous behavior detected.

To understand the details of the anomaly, we examine the output of Amazon Lookout for Equipment in the AWS Management Console. The dashboard shows all the anomalies that were detected in the time window we examined – including the anomaly that led to the “Check Battery” light turning red. Selecting the anomaly shown in the Grafana dashboard we see that the four sensors on which the model was trained all show anomalous behavior. The Amazon Lookout for Equipment dashboard shows the relative contribution of each sensor to this anomaly in per cent. Anomalous behavior of the battery voltage and the battery SoC are the leading indicator in this anomaly.

This is consistent with how we introduced the anomaly in the synthetic dataset and trained the model. We first used periods of normal operation to train an unsupervised Amazon Lookout for Equipment model on the four sensors shown. After that, we evaluated this model on a new dataset shown in the Amazon Lookout for Equipment dashboard above, where we manually induced faults. Specifically, we introduced an energy loss term in the data leading to a subtle faster decline of the SoC that also affects the other sensors. It would be challenging to design a rules-based system to detect this anomaly early enough to avoid further damage to the car – particularly if such behavior has not been observed before. However, Amazon Lookout for Equipment does initially detect some anomalous periods and from a certain point onwards flags anomalies over the whole remaining time. Of course, the contributions of each sensor to an anomaly could also be displayed in the Grafana dashboard.

3. Failure Prediction

Another common use case for industrial equipment is to predict end of life of components in order to preplan and schedule maintenance. Developing models for failure prediction can be very challenging and typically requires custom analysis for failure patterns for the specific equipment under a wide variety of different operating conditions. For this use case, AWS offers Amazon SageMaker, a fully managed service to help train, build, and deploy machine learning models. We will show how to integrate Amazon SageMaker with AWS IoT TwinMaker in the next section when we discuss the solution architecture.

For our example, we created a synthetic battery sensor dataset that was manually labeled with its remaining useful life (RUL). More specifically, we calculated an energy loss term in our synthetic battery model to create datasets of batteries with different RUL and manually associated larger energy losses with shorter RULs. In real life such a labeled dataset could be created by engineers analyzing data of batteries that have reached their end of life. We used an XGBoost algorithm to predict RUL based on 2-minute batches of sensor data as input. The model takes features derived from these batches as input. For example, we smoothed the sensor data using rolling averages and compared the sensor data between the beginning and the end of the 2-minute batch. Note that we can make predictions at a granularity of less than 2 minutes by using a rolling window for prediction. In our example, the Remaining Useful Life of the battery is displayed in the dashboard under the Check Battery symbol. This vehicle is in a dire situation with a prediction of imminent battery failure!

4. Architecture

The solution architecture for the L3 Predictive DT use cases builds on the solution developed for the L2 Informative DT and is shown in below. The core of the architecture focuses on ingesting the synthetic data representing real electric vehicle data streams using an AWS Lambda function. The vehicle data including vehicle speed, fluid levels, battery temperature, tire pressure, seatbelt and transmission status, battery charge, and additional parameters are collected and stored using AWS IoT SiteWise. Historical maintenance data and upcoming scheduled maintenance activities are generated in AWS IoT Core and stored in Amazon Timestream. AWS IoT TwinMaker is used to access data from multiple data sources. The time series data stored in AWS IoT SiteWise is accessed through the built-in AWS IoT SiteWise connector, and the maintenance data is accessed via a custom data connector for Timestream.

For the L3 virtual sensor application, we extended the core architecture to use AWS Glue to integrate the Maplesoft EV model by using the AWS IoT TwinMaker Flink library as a custom connector in Amazon Kinesis Data Analytics. For anomaly detection, we first exported the sensor data to S3 for off line training (not shown in diagram). The trained models are made available via Amazon Lookout for Equipment to enable predictions on batches of sensor data via a scheduler. Lambda functions prepare the data for the models and process their predictions. We then feed these predictions back to AWS IoT SiteWise from where they are forwarded to AWS IoT TwinMaker and displayed in the Grafana Dashboard. For failure prediction, we first exported the sensor data to S3 for training and labeled using Amazon SageMaker Ground Truth. We then trained the model using an Amazon SageMaker training job and deployed an inference endpoint for the resulting model. We then placed the endpoint inside a Lambda function that is triggered by a scheduler for batch inferencing. We feed the resulting predictions back to AWS IoT SiteWise from where they are forwarded to AWS IoT TwinMaker and displayed in the Grafana Dashboard.

5. Operationalizing L3 Digital Twins: data, models, and key challenges

Over the past 20 years, advances in predictive modeling methods using machine learning, physics-based models, and hybrid models have improved the reliability of predictions to be operationally useful. Our experience, however, is that most prediction efforts still fail because of inadequate operational practices around deploying the model into business use.

For example, with virtual sensors, the key task is developing and deploying a validated model in an integrated data pipeline and modeling workflow. From a cloud-architecture perspective, these workflows are straightforward to implement as shown in the EV example above. The bigger challenges are on the operational side. First, building and validating a virtual sensor model for complex equipment can take years. Virtual sensors are often used for quantities that cannot be measured by sensors, so by definition there is no real-world validation data. As a result, the validation is often done in a research laboratory running experiments on prototype hardware using a few very expensive sensors or visual inspections for limited validation data to anchor the model. Second, once deployed, the virtual sensor only works if the data pipeline is robust and provides the model with the data it needs. This sounds obvious, but operationally can be a challenge. Poor real-world sensor readings, data drop-outs, incorrectly tagged data, site-to-site variations in data-tags and changes made to the control system tags during overhauls are often causes for tripping up a virtual sensor. Insuring good quality and consistent data is foundationally a business operations challenge. Organizations must define standards, quality-checking procedures, and training programs for the technicians who are working on the equipment. Technology will not overcome poor operational practices in gathering the data.

With anomaly detection and failure predictions, the data challenges are even greater. Engineering leaders are led to believe that their company is sitting on a gold-mine of data and wonder why their data science teams are not delivering. In practice, these data pipelines are indeed robust, but were created for entirely different applications. For example, data pipelines for regulatory or performance monitoring are not necessarily suitable for anomaly detection and failure predictions. Since anomaly detection algorithms are looking for patterns in the data, issues such as sensor mis-readings, data dropouts, and data tagging issues can render the prediction models useless, but that same data can be acceptable for other use cases. Another common challenge is that data pipelines that are thought to be fully automated, are in fact not. Undocumented manual data corrections requiring human judgement are typically only discovered when the workflow is automated for scaling and is found not to work. Lastly, for industrial assets, failure prediction models rely on manually collected inspection data since it provides the most direct observation of the actual condition of the equipment. In our experience, the operational processes around collecting, interpreting, storing and integrating inspection data are not robust enough to support failure models. For example, we have seen inspection data show up in the system months after it was collected, long after the equipment has already failed. Or the inspection data consists of handwritten notes attached to an incorrectly completed inspection data record or associated with the wrong piece of equipment. Even the best predictive models will fail when provided incorrect data.

For L3 Predictive Digital Twins, we encourage our customers to develop and validate the business operations to support the Digital Twin’s data needs at the same that the engineering teams are building the Digital Twins themselves. Having an end-to-end workflow mindset from data collection through to predictions and acting on the predictions is critical for success.

Summary

In this blog we described the L3 Predictive level by walking through the use cases of a virtual sensor, anomaly detection, and failure prediction. We also discussed some of the operational challenges in implementing the necessary business processes to support the data needs of an L3 Digital Twin. In a prior blog, we described the L1 Descriptive and the L2 Informative levels. In a future blog, we will extend the EV use case to demonstrate L4 Living Digital Twins. At AWS, we’re excited to work with customers as they embark on their Digital Twin journey across all four Digital Twin levels, and encourage you to learn more about our new AWS IoT TwinMaker service on our website.

About the authors

Dr. Adam Rasheed is the Head of Autonomous Computing at AWS, where he is developing new markets for HPC-ML workflows for autonomous systems. He has 25+ years experience in mid-stage technology development spanning both industrial and digital domains, including 10+ years developing digital twins in the aviation, energy, oil & gas, and renewables industries. Dr. Rasheed obtained his Ph.D. from Caltech where he studied experimental hypervelocity aerothermodynamics (orbital reentry heating). Recognized by MIT Technology Review Magazine as one of the “World’s Top 35 Innovators”, he was also awarded the AIAA Lawrence Sperry Award, an industry award for early career contributions in aeronautics. He has 32+ issued patents and 125+ technical publications relating to industrial analytics, operations optimization, artificial lift, pulse detonation, hypersonics, shock-wave induced mixing, space medicine, and innovation.

Seibou Gounteni is a Specialist Solutions Architect for IoT at Amazon Web Services (AWS). He helps customers architect, develop, operate scalable and highly innovative solutions using the depth and breadth of AWS platform capabilities to deliver measurable business outcomes. Seibou is an instrumentation engineer with over 10 years experience in digital platforms, smart manufacturing, energy management, industrial automation and IT/OT systems across a diverse range of industries.

Dr. David Sauerwein is a Data Scientist at AWS Professional Services, where he enables customers on their AI/ML journey on the AWS cloud. David focuses on forecasting, digital twins and quantum computation. He has a PhD in quantum information theory.

 

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As many as six persons including a female member of the National Youths Service Corps; a pregnant woman; a mother and her two children; and a 70-year-old man, have been feared dead in a passenger boat accident in Bayelsa State.

They were said to have drowned as a result of the incident which occurred on Saturday between Otuan and Ayama in Southern Ijaw Local Government Area of the state. 

According to reports, the boat which had 15 passengers on board, rammed into a barrier in the waters and sank while approaching Ayama to berth, amidst a heavy rainstorm.

It was gathered that the passengers were covered with a tarpaulin to protect them from the rainstorm when the accident happened.

The corpses of the casualties and the boats were still missing even as a search team had been deployed in the area as of the time of this report.

The unit chairman of the Maritime Workers Union of Nigeria at Otuan community, Joseph Shadrach, said the boat driver, Christopher Lucky, had been detained by the police in Ayama for allegedly driving under poor visibility.

He claimed that preliminary investigation showed that the weather was bad due to the rainstorm, which had started before the boat moved.

He said, “I was the chairman of the union but some persons who claimed to know more than themselves have been taking charge of the Otuan unit.

“If I had been around, I would not have allowed the loading of the boat due to poor visibility and bad weather. I was told a corps member, two children, and a woman died.”

Confirming the boat accident, the state chairman of MWUN, Ipigansi Ogoniba, said that six passengers had been confirmed dead.

“Six persons died. When the boat got to Ayama, it had a mishap. One corpse was recovered and handed to the family for proper burial. While three adults and three children were still missing or probably dead,” he said.

Ogoniba said the state NYSC authorities had been contacted through the community liaison officer on the identity of the deceased female corps member, adding that “they are still searching for her corpse and we have reached out to the NYSC for her identity.”

The spokesman for the State Police Command, Asinim Butswat, said that a search team comprising the marine police, MWUN and divers was combing the waters to recover the bodies of the victims.

He also confirmed that the boat driver had been detained for questioning over the circumstances that caused the accident.

The Public Relations Officer of the state NYSC, Matthew Ngobua, said the state coordinator of the scheme had departed for the scene of the incident for identification of the deceased corps member.

He said, “We got the information and the State Coordinator and other staff left the Orientation (Camp) and went to the place. 

“The State Coordinator told me that the corps member’s name is said to be on the passenger manifest and the said corps member is not at the lodge.

“However, we will make a detailed report after the search and rescue or when the body is recovered. The NYSC will make an official brief when the body is sighted and properly identified.”

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The Independent National Electoral Commission (INEC) has addressed the issue of using placeholder candidate.

Recall that some presidential candidates submitted the name of a member of their political party as a placeholder to meet the deadline of INEC.

Presidential candidates of the All Progressives Congress, Bola Tinubu and Peter Obi of the Labour Party, both used placeholders.

Reacting on Monday, INEC’s National Commissioner of Information and Voter Education, Festus Okoye while speaking on Arise TV said the running mates can only be substituted if only they write INEC, attaching an affidavit.

Okoye said, “The law says that as a presidential candidate, you must nominate an associate to run with you and as far as the Independent National Electoral Commission is concerned, the presidential candidates have submitted their associates to run with them in the presidential election.

 

“As far as we are concerned, there’s no form submitted by the presidential candidate where they said ‘we’re submitting this person’s name as a place or space holder’.

 

“The issue of space or place holder is a unique Nigerian invention that has no place in our constitutional and legal framework.

 

“For there to be a substitution of the candidate, the Vice presidential candidate must write to INEC, with an affidavit stating that he is withdrawing from the race within the time frame provided by the law. That’s the only way there can be a substitution of candidates.”

 

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The Criminal court 1 in Benin city, Edo state, has sentenced Dominion Okoro the housemaid who killed Madam Maria Igbinedion, mother of ex-governor of Edo state, Lucky Igbinedion, to death by hanging.

Recall that the assailant had murdered the 85-year-old woman at her residence in GRA, Benin City, on December 2, 2021.

Okoro said she had killed her employer because she wanted to steal her jewelry and money.

She said: “I killed Mama (Madam Maria Igbinedion) to carry her money. She did not offend me. While she was sleeping on her bed, around 12:01 a.m. on December 2, 2021, I used a stool to hit her on the head and she was shouting for help, but only the gateman was around and he did not hear the shout. Mama later died.

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It’s no secret that 2022 has been a brutal year for software companies. Valuations have been slashed, inflation keeps climbing and layoff announcements abound. The whispers of a potential recession have morphed into full-throated shouts. Once high-flying companies now struggle to attract new capital to sustain their growth, causing SaaS executives to pivot hard toward profitability.

The silver lining on this dark cloud: Downturns coincide with a rush of startup activity. Folks who’ve been laid off or have woken up to realize their stock options are suddenly worthless will opt to bet on themselves. They’ll finally take a chance turning that nagging idea into an actual product. And they’ll build a company with the discipline that comes with a macro environment that prizes capital efficiency rather than growth at any cost.

In short, the best software companies of the 2020s will be built over the next 12 months. But the way to build and scale a standout company in the 2020s doesn’t look like it did in the past.

Let’s unpack what’s changed and principles for how to build, distribute and monetize products in this new era, the Age of Connected Work. I’ll focus on six of the most fundamental principles that apply to nearly every PLG product.

Build for the user

The classic B2B playbook focused almost exclusively on executive buyers. The actual end users of a product were an afterthought.

Now, the B2B buyer journey starts with the end user. They discover software products, share them with colleagues and tell their boss what to buy. Product engagement sets the tone and then buyers follow users. You should think deeply about the needs and experiences of the folks who will actually use your products, not just those who will sign the PO.

Build to be discovered

Software companies used to throw money and bodies at getting noticed by big company executives. Expensive customer acquisition costs weren’t an issue as long as you could maintain healthy top-line growth. Now, prospective buyers feel bombarded by the constant barrage and want to opt out (more and more are even using tools like Gated to mute the unwanted emails).

Healthcare and life sciences organizations are no strangers to change. But as the pandemic took hold, it forced change in a way the industry hadn’t seen before. Much of the workforce went remote seemingly overnight, and companies had to adapt — and innovate — fast.

In fact, according to a survey of healthcare and life sciences leaders, 50% said remote and hybrid work settings were a top internal disruption factor at the start of the pandemic. The lesson was clear: change management in health should put employee experiences front and center no matter where they work. This way we successfully drive innovation, while accounting for any change fatigue and burnout.

Drive innovation through disruption

The modern workplace has been shaken by change, and the health industry has not been immune to these developments. See how, with the right culture, tools, and processes, you can find ways to humanize that change and use it as a springboard to drive innovation.

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We recently visited oral care and telehealth pioneer SmileDirectClub in Nashville, Tenn., to learn how its people-first culture helped drive rapid innovation during the pandemic. We spoke with Team Captain Kayla Spicer and Global Head of Supply Chain Dan Baker about how the company is not only invested in delivering quality products and services to 1.7 million smiles around the world, but to its own teams as well.

Once we launched Salesforce, we were able to do a lot of different things like video calls. That gives our customers access to care that you would never imagine.

Kayla Spicer, Team Captain, SmileDirectClub

While the pandemic disrupted everything from processes and training to working with customers, SmileDirectClub’s people-first mentality helped it act quickly to empower teams no matter where they were. With the help of Salesforce, SmileDirectClub was able to unify its systems and launch game-changing video calling technology that improved both the customer and employee experience. 

“With Salesforce, we were able to combine all of the systems that we use in one place,” Spicer said. “Once we launched Salesforce, we were able to do a lot of different things like video calls. That gives our customers access to care that you would never imagine.” 

Culture-building activities help teams build confidence

SmileDirectClub teams used video-calling technology to infuse fun into their training, like showcasing their pets on camera. These culture-building activities ultimately helped team members build confidence to use the tool effectively with customers. 

This new technology also helped SmileDirectClub quickly pivot during the pandemic to deliver training and materials remotely, which allowed flexible and remote working options.

It comes down to trust. It’s about creating a workplace where team members’ opinions are heard and listened to.

Dan Baker, Global Head of Supply Chain, SmileDirectClub

For SmileDirectClub, successful change management in health is based on investing in people and developing trust. While technologies like artificial intelligence (AI) and automation are certainly driving change, Baker noted that it’s actually the people making innovation possible. “I think it comes down to trust,” Baker told me. “It’s about creating a workplace where team members’ opinions are heard and listened to, and then they can then see the evidence of their ideas making a difference.” 

And SmileDirectClub isn’t just talk. Recently, the company launched a new aligner manufacturing technology that incorporates automation. By listening to its team members, the company was able to drive incremental productivity, without increasing workload. The process is a testament to a culture that believes in continual improvement and that good ideas can come from anywhere.

Maintaining human connections at Deloitte Digital

Implementing human-first change management practices also resonates with experience consultancy Deloitte Digital. We went to the Boston office of Chief Experience Officer Amelia Dunlop, who shared how her early career was impacted by her doubts about her own self-worth. It’s a concern shared by many professionals, she added.

To drive great work through authenticity, Deloitte Digital was inspired to start with its own people. For example, the company developed a frequent employee survey that asks a simple question — “what can we do to elevate your experience?” — and then acts on the answers.

We’re more connected than ever (digitally), with friends and colleagues from all over the world, but we feel less of that human connection that we so need.

Amelia Dunop, Chief Experience Officer, Deloitte Digital

Deloitte Digital also attributes burnout as a big workplace challenge. To avoid it, Dunlop said it’s important to create and maintain human connections so that we don’t suffer from what she calls the “human experience debt” sparked by the pandemic. 

“In ways, we’re more connected than ever (digitally), with friends and colleagues from all over the world, but we feel less of that human connection that we so need,” she explained. “And because of that human experience debt, we need to do more to show up and feel more human.”

Successful change management in health

Disruptions in healthcare and life sciences may be inevitable, but as we’ve learned from SmileDirectClub and Deloitte Digital, organizations can continue to innovate and transform by investing in their employees first. With people at the core of every business, it’s clear we need each other to succeed. Improving the human experience can lead to higher productivity, boosted morale, and ultimately, a more rewarding and adaptive environment. 

See how industry leaders are driving change across the globe

Gain industry insights and learn how Trailblazers are driving the latest innovations. To learn more about what organizations are doing to humanize change management in health, watch our latest video.

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Robb Elementary School in Uvalde, Texas, the school where a gunman killed 19 students and two teachers - will be demolished, the city's mayor has said.


Mayor Don McLaughlin made the claim on Tuesday, June 21 at an emotional council meeting with residents demanding answers over the shooting.


The mayor however did not say when the school would be demolished.


Public anger has risen in among the town's residents since the May rampage, with police accused of waiting over an hour to confront the assailant.






Robb Elementary has nearly 600 students in the second, third and fourth grades

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Popular singer and songwriter, Adedamola Adefolahan, popularly known as Fireboy DML has affirmed that Nigerians make the best Afrobeat music.

As a performing artist at BET Awards show, The YBNL records Afrobeat star declared this during an interview with a journalist during the pre-show rituals.

RELATED ARTICLE: Top Nigerian artists, Wizkid, Davido, and Simi among others bag vast nominations at the NET Honours people’s choice Awards (Full List)

When asked why the influence of Afrobeat music is coming from Nigeria.

The Peru crooner confessed that Nigerians make the best Afrobeat music. He also unveiled that there are billions of talents in the country.

Watch the video below

“We make the best Afrobeat music…” @fireboydml on his BET nomination #BETAwards

pic.twitter.com/XcytD4LKbz

— Pulse Nigeria (@PulseNigeria247) June 26, 2022

The post “We Make the Best Afrobeat music” – Nigerian singer, Fireboy DML affixowledges Nigeria Afrobeat singers (Video) appeared first on TrendyHipHop.

Nollywood actress, Carolyn Hutchins has debunked falsifying her age and seemingly responded to critics who jabbed her for allegedly faking her age on her birthday post.

The Real Housewives Of Lagos star had taken to her official Instagram account and shared stunning pictures of her birthday.

RELATED ARTICLE: 2023 Election: Nollywood actress, Tonto Dikeh emerges as ADC deputy Gubernatorial candidate in Rivers State

In her birthday post, the mother of one stated that she was happy as she turned 35 years old.

This triggered trolls who took to the comment section of her birthday post and jabbed her for allegedly faking her actual age.

Reacting to the critics, Hutchins debunked faking her actual age and shared the above picture she took with her beautiful mother and her daughter and stated their actual age respectively.

In her lengthy words, she wrote:

“Trilogy. This is my mum who is in her 50’s, so how in God’s name will I be 46? Am I reducing my age because I need a job in a company? I am the CEO of mine.

Reducing my age for Nollywood? An actor can act in both young and old roles. Makeup can be applied. I am reducing my age because of school? I have enough certificates already.

Reducing my age because of a relationship? A good man will love his woman regardless of her age. Reduce my age for your validation? I don’t give two F’s about you, sugar.

Not everyone in a class is the same age. In secondary school, I had people 5 years older than me in my class. In university, my assistant class rep was 45 years old. In my master’s, my class rep was 23 and I 34. In my MBA, I had both young and old.

Just because you acted in a movie with me does NOT mean we are the same age. You are asking for my birth certificate, Have you seen your mother and father’s birth certificate before?

That it took you time to achieve something in your life does not mean I have to run your race. We have different glory and destiny. That you started school late and failed the entrance exam a couple of times is none of my business. We don’t have the same IQ.

Stupid people saying they watched me as a kid. I was in the industry for only two years before my marriage. Bomboclat can’t stand my glory.

Are you aware Wikipedia can be created and edited by ANYONE? And not everything on Google is a FACT? Even Christ was married on Google. To the clowns talking unintelligently about my age, mind the business that pays you.

Any age, race, or class can act in a movie. Anyone can be a star at any age; no rules to follow. Yes, I am 35 years old and this will be the last time I will address this.

I don’t need to be an old woman to achieve anything or be an actress. Success and luck come to anyone when it is their time. Focus on making money.”

See her post below…

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A post shared by Caroline Danjuma (@carolynahutchings)

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The Winner of the BBNaija season 6 edition, Hazel Oyeze Onou, better known as Whitemoney has revealed why he may end up not getting married.

The Enugu-born businessman made this disclosure via his Twitter handle where he lamented over the level of deceit, betrayal, lies, cheating and unfaithfulness he has witnessed in the past 2 months; all of which has made him have a second thought about marriage.

According to him, one needs to hear from God directly before letting emotions get hold of him/her.

In his words:

“E be like say last last i will just date/marry myself oo cause the level of deceit, betrayals, lies, cheating & unfaithfulness I have witnessed in the past 2 months ehh hmm one need to hear from God directly before your emotions oo, speak to me Lord, I’m all ears 👂🤪😜”

See his tweet below:

E be like say last last i will just date/marry myself oo cause the level of deceit, betrayals, lies, cheating & unfaithfulness I have witnessed in the past 2 months ehh hmm one need to hear from God directly before your emotions oo, speak to me Lord, I’m all ears 👂🤪😜

— Hazel Oyeze Onou (@itswhitemoney) June 29, 2022

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Controversial relationship coach, Blessing Okoro, has sparked reactions after she flaunted a video of herself unclad showing off her new backside days after undergoing liposuction surgery.

TrendyHipHop had reported that the social media influencer went for a successful liposuction surgery as she shared the post-liposuction pictures via her Instagram account.

 

RELATED ARTICLE: “Goodbye to my old body” – Relationship expert, Blessing Okoro spills over her successful liposuction surgery (Photos)

However, on Wednesday morning, Blessing shared a video of herself wearing only underwear to give a full view of her enhanced curves.

She shared the video of her bare butt to shun unbelievers who doubted that she did liposuction of her bare butt.

Watch some of her videos below…

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A post shared by Blessing Okoro (@officialblessingceo)

 

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A post shared by Blessing Okoro (@officialblessingceo)

 

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A post shared by Blessing Okoro (@officialblessingceo)

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As marketers, our goal is to attract people to our brand. But not just any people: we want to draw prospects with a high likelihood of becoming customers. Good lead-generation tactics can help. 

We define lead generation as the process of creating consumer interest for a service or product, with the end goal of turning that interest into revenue.

Leads can be generated when a consumer downloads a piece of content, signs up for a free trial, or creates an account for a free product, for example. They come from organic channels, such as Google search, and paid channels, such as advertising campaigns. My job at Salesforce focuses on paid channels.

Generating mass appeal is nice, but here’s the thing: not all leads are created equal. Running a strong lead-generation program is not about the quantity of leads you generate. It’s about ensuring quality leads by targeting the right people, at the right time in their customer journey, on the right channel, and then delivering the right content to them.

Does this keep you up at night? Don’t worry. Here are three tips to help you improve your lead generation tactics, using examples from how we do things here at Salesforce.

Develop lead generation tactics that drive business

Salesforce’s Zand Ushijima explains how to attract prospects who have a higher chance of converting to customers.

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1.  How to measure your lead generation tactics

A North Star metric is a key indicator of business growth and can be tied to increases in product engagement, and eventually, revenue. It’s something your entire organization can rally behind.

My team’s North Star metric is when an opportunity reaches the second stage of our sales funnel. At that point, a prospect has been vetted by our sales development team and verified and accepted by an account executive. 

The Sales team will estimate the potential revenue from the opportunity, allowing us to also begin tracking a return on our investment. Then we start to build out our measurement framework. We use this metric to measure the success of our lead generation tactics and improve programs.

When measuring, it’s important to gauge which sources are bringing in high-quality leads. Are they social platforms like LinkedIn, email newsletters, or ad partners like Google? Also, make sure the money you’re spending on ads is generating high-quality users.

An example of a high-quality lead is a “hand raiser” – someone who engages with the brand or asks for information (say, requesting a demo or free trial). A passive lead is someone who provides contact info to download an ebook but hasn’t expressed other interest. We pay close attention to hand-raiser leads because they have a higher chance of becoming customers.

We focus on content performance from the highest-quality sources of leads. If traffic is dropping from a top-quality lead source, we react quickly because of the expected downstream effect. Possible tweaks include changing our ad design or ad copy, adjusting our paid investment or updating a call to action (CTA).

2.  Develop strategies to get the right leads

You may hear people say that a “lead is a lead,” but that’s a big misconception. 

Customer insight and interest is valuable, and social platforms like Facebook and LinkedIn generate revenue by offering targeted advertising that uses the customer data they’ve collected. So, a more targeted campaign on LinkedIn will cost significantly more than a general banner ad.

Some leads can be less costly and have huge scale, but are low quality because they aren’t reaching people who are looking for your products or services (and therefore less likely to convert).

Other leads are more expensive but target people who are ready to buy. And then there is everyone in between.

The various channels, targeting options, and content that you pair together to acquire leads can produce hundreds or even thousands of iterations. You have to understand which of these lead generation tactics produces quality leads and at what cost.

Here’s an example of how you can make this work. On LinkedIn, you target sales managers within the financial services industry who have interacted with your business once in the past. You serve them a customer story from a banking customer. These leads might be more expensive, but they’re highly likely to convert, so the cost is worth it.

3. How to test your lead generation tactics

Improving the efficiency of your lead generation tactics is important, no matter if you’re a one-person team at a startup or part of a larger team at an enterprise.

At Salesforce, we improve long-term efficiency by running tests on our paid ads. You can run a test by delivering two or more versions of ad copy or form pages to different segments of your audience at the same time and seeing which ones perform best.  

These tests are done on our highest-trafficked content. We’ve found that even the most basic changes have driven significant returns – and then we can apply those changes to other programs to make them more efficient as well.

For example, we ran a simple test changing the layout of the several fields in a form that readers filled out to access a piece of content. It was simple — just shortening the vertical length of the form. This change increased form submission on desktop computers by 10%. We now do this on all our forms.

Again, not all tests need to be big overhauls of your content. What matters most is that when you do find wins, they are used across many of your programs.

Building the right lead-generation tactics for your business takes some work. But when done efficiently, your tactics will save you money and time while helping to bring in new revenue. Good luck!

Learn the basics of lead generation

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Fast-rising songstress Maudlyn Abajuo comes out with a brand new track titled “GOD IS FAITHFUL” off her debut album set to drop later this year.

According to Maudlyn she said it’s a new sound of God’s faithfulness and how He has assured us as believers that he is faithful to honor his word more than his name. She talks about holding onto God no matter the situation we find ourselves knowing that He is faithful to see us through till the end.

“God is Faithful” is available on all international and local platforms.

STREAM/DOWNLOAD HERE

Listen, Download & Enjoy Below;

DOWNLOAD MP3

Follow Maudlyn on all socials;
Facebook: Maudlyn Abajuo
Instagram: @maudlynabajuo
Tiktok: @maudlynabajuo

The post Maudlyn Abajuo – “God Is Faithful” first appeared on tooXclusive.

Egbunike Peter Zeluwa popularly known as “ROCKBOY” is a Nigerian music artiste born on the 27th of September 1999, who hails from Ovim in Isuikwuato LGA of Abia state. He started singing at the age of seven (7), a graduate of International Relations from ABIA STATE UNIVERSITY. He just dropped this new single which you can’t say No to “MYSTERY”

As Produced by TRIP, Listen and Enjoy!

DOWNLOAD MP3

Available On All Digital Stores HERE:  

 

The post Rockboy – Mystery first appeared on tooXclusive.

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The Nigerian singer-songwriter and multi-instrumentalist Akjoel has released a new masterpiece titled Dada Acapella Cover.

Another cover of Young Johns’ still-popular song, “Dada Acapella Cover” is here.

In conclusion, the fantastic new tune is a fantastic song that every music enthusiast should add to their playlist.

Take a listen and let us know what you think in the comment section below!

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The post Akjoel – Dada Acapella Cover Ft. Young John appeared first on TrendyHipHop.

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M.I. Abaga, a gifted Nigerian singer, and songwriter has just dropped a new, amazing single called “Too Sweet.”

“Too Sweet” features the artist’s collaboration with Olamide, head honcho of the record label YBNL.

One may reasonably assume that this legendary album is a fair picture of what great music should sound like based on the groove of the song “Too Sweet,” which features outstanding and fascinating lyrics and vocals.

The singers clearly tried to do something new and interesting with this album, and the results aren’t bad.

That music will stick with you, and it has all the makings of a new favorite. We recommend adding this song to your playlist.

Listen and then share your thoughts in the section below!

 

DOWNLOAD MP3

The post M.I Abaga – Too Sweet ft. Olamide appeared first on TrendyHipHop.

Burna Boy, a Nigerian singer, just released a new song called “Common Person,” and you asked for the lyrics, so here they are.

Below are the song and lyrics in case you haven’t already downloaded it.

GET THE MAIN SONG CLICK HERE!

LYRICS:

Bong killa
Na me wey dey clean una house
Na me wey dey wash una car
Na me wey dey cook ununu
Na me wey dey wash una cloth
Na me wey dey work for your shop
Na me wey dey hawk ununu

I be common person but my happiness oh still be my own
Everybody get role no mean say your own role pass e my own

The post Burna Boy – Common Person (Lyrics) appeared first on TrendyHipHop.