Simply Contact, a Ukrainian outsourcing contact center, joins PACE - The Professional Association for Customer Engagement.
PACE is a non-profit trade association dedicated to customer service. It aims to help companies and their teams stay current with the latest technology advancements, new regulations, and trends in the contact center industry.
Technology can be a two-edged sword. It can deliver incredible results and create unique problems. The customer experience (CX) sector, in particular, has been heavily impacted by technology for quite some time.
Just because you’re using customer relationship management (CRM) tech, doesn’t mean it’s working, though. Here are a few questions to ask yourself to see if your tech is making or breaking your customer’s experience.
#customer-experience #customer-service #technology #tech #customer-support #customer-engagement #bus #customer-support-chatbots
One of the most frequently used transformations in Apache Spark is Join operation. Joins in Apache Spark allow the developer to combine two or more data frames based on certain (sortable) keys. The syntax for writing a join operation is simple but some times what goes on behind the curtain is lost. Internally, for Joins Apache Spark proposes a couple of Algorithms and then chooses one of them. Not knowing what these internal algorithms are, and which one does spark choose might make a simple Join operation expensive.
While opting for a Join Algorithm, Spark looks at the size of the data frames involved. It considers the Join type and condition specified, and hint (if any) to finally decide upon the algorithm to use. In most of the cases, Sort Merge join and Shuffle Hash join are the two major power horses that drive the Spark SQL joins. But if spark finds the size of one of the data frames less than a certain threshold, Spark puts up Broadcast Join as it’s top contender.
Looking at the Physical plan of a Join operation, a Broadcast Hash Join in Spark looks like this
The above plan shows that the data frame from one of the branches broadcasts to every node containing the other data frame. In each node, Spark then performs the final Join operation. This is Spark’s per-node communication strategy.
Spark uses the Broadcast Hash Join when one of the data frame’s size is less than the threshold set in spark.sql.autoBroadcastJoinThreshold. It’s default value is 10 Mb, but can be changed using the following code
spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 100 * 1024 * 1024)
This algorithm has the advantage that the other side of the join doesn’t require any shuffle. If this other side is very large, not doing the shuffle will bring notable speed-up as compared to other algorithms that would have to do the shuffle.
Broadcasting large datasets can also lead to timeout errors. A configuration spark.sql.broadcastTimeout sets the maximum time that a broadcast operation should take, past which the operation fails. The default timeout value is 5 minutes, but it can be set as follows:
If neither of the data frames can be broadcasted, then Spark resorts to Sort Merge Join. This algorithm uses the node-node communication strategy, where Spark shuffles the data across the cluster.
Sort Merge Join requires both sides of the join to have correct partitioning and order. Generally, this is ensured by** shuffle and sort** in both branches of the join as depicted below
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As COVID-19 staggeringly lands blows to nations across the world, governments are considering ways to see their citizens through this pandemic. At the moment, a WHO situation report clocks the number of confirmed cases above two million along with more than one hundred thousand deaths. With vaccines dubbed as the best possible chance to tackle COVID-19 having no precise time frame of being ready, the talk is quickly shifting away to Contact Tracing Applications.
Contact tracing apps are digital solutions that use mobile technology to power the process of manual contact tracing. The apps follow a user’s movement, either by the use of Bluetooth technology, QR codes, or geo-location data while also tracking and keeping data from other user phones nearby. If one user gets diagnosed, the apps alert other users that they may have been exposed to the virus. As such, Contact Tracing Applications are being welcomed and perceived as an important approach to stem the spread of COVID-19 by providing a more accurate platform with data and information about affected individuals.
As mentioned above, contact tracing apps leverage mobile technology to trace cases of possible infection more accurately. But how exactly? Once installed and operative, the phone runs the app simultaneously with Bluetooth or location data to transmit signals with unique keys or IDs to phones in the designated range of connection. Similarly, the other phones with the app installed to detect and send back the signals.
For instance, if ‘Individual A’ has the app installed and goes outdoors to run some errands, they will interact with other individuals. In such a case, supposing all the other individuals had functional Contact Tracing Apps, each phone would exchange and store the contact data anonymously. It is important to note that the data collected only covers the app range distance to disregard irrelevant contacts and that their keys repeatedly change as individuals move. In any event that ‘Individual A’ tests positive for COVID-19 through confirmed tests, users who were previously within the proximity of ‘Individual A’ are alerted. Consequently, they are notified to check for symptoms, self-isolate, or get tested. Each time a person tests positive, the app notifies and advises the affected individuals.
In a nutshell, Contact Tracing Apps automate and supplement the traditional concept of tracing contacts to achieve extensive and realistic results in the least time possible.
Contract Tracing Apps are assets that offer indispensable solutions to health institutions and the public against COVID-19. There are several reasons why many governments are urging their citizens to use digital contact tracing apps to combat the spread of COVID-19. They include:
Currently, the role of contact tracing apps is limited to accurately identifying infected individuals and their contacts as well as facilitating a quicker response to the Covid-19 threat.
Beyond that, the use of contact tracing apps is projected to take a different turn. One key area bound to change is how people’s privacy is handled. Tech institutions are under growing pressure to devise ways to develop privacy-preserving Contact Tracing Apps.
This will earn the users-trust, which is a pillar for these apps to help contain the disease. Technically, the technology will also have to improve drastically. The apps will have to seamlessly integrate with the user’s phone lifestyle causing minimal or no interference. With most applications having an open-source code, Artificial Intelligence, Beacon Technology, and Big Data solutions will be increasingly harnessed to power and improve them. The apps may also cut across various types of industries apart from health institutions.
Contact Tracing Apps will effectively help stem lowering the cases of COVID-19. By using the apps, officials are able to monitor high-risk individuals easily. Also, should any new case arise, both users and health officials get notified they will swiftly act to trace, test, or isolate infected individuals.
Unlike traditional contact tracing, which may not get all contacts, these apps ensure that once Covid-19 cases are detected, they are all treated early, and those other individuals are not exposed to the infection. They also ward off users from high-risk areas. In the long run, they help break the COVID-19 chain by preventing further spread. Illustratively, an online publication by CNBC states that more than 500,000 using a Singapore-registered mobile number downloaded the TraceTogether app within the first 24 hours of its launch. Subsequently, together with other government efforts, Singapore has since lowered the infection rate and eased restrictions.
If Contact Tracing Apps are implemented and used alongside other policies, we may as well be a few steps way to curbing this virus.
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Customer Feedback Tool | Fynzo online customer feedback comes with Android, iOS app. Collect feedback from your customers with tablets or send them feedback links.
Visit page for more information: https://www.fynzo.com/feedback
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Digital transformation gives a personalized look into the customer’s purchasing habits along with their likes and dislikes. Making it easy for brands to provide a tailor-made premium customer experience based on personal preference & unspoken need.
Ever since smartphones became a part and parcel of human life, people have been a part of a digital network that connects them to friends, businesses, colleagues, and peers.
People don’t just buy products now, they connect with brands, register on their web portals, use their application, give email addresses, and phone numbers at cash counters.
They expect brands to understand their individual needs & answer back when they complain. This has encouraged brands to embrace digital transformation and reinvent customer success.
#digital-transformation #customer-experience #digital-strategy #digital-transformation-guide #customer-satisfaction #customer-engagement #customer-satisfaction-rates #net-promoter-score